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Course | Summary | Bestseller | Rating | Rated By ? | How Many Students ? | Created By | Last Updated | Language | Current Price (May vary as per Country / Offer) | Course Length | Buy | TrainerFilter | Trainer Profile | Trainer Rating | How Many Students Rated This Trainer | Total Students Taught By This Trainer | Total Courses By The Trainer | Course Description | TrainerProfileOnUdemy | Trainer Profile Summary | Curent time | Other Categories | Trainer Level | Maths | Statistics | DSA | Course By Rating | How Many Rated The Course ? | How Many Took The Course ? | Trainer Overall Rating | How Many Reviewed The Trainer ? | Total Students Taught by the Trainer | labguage | ex1 | ex2 | ex3 | ex4 | ex5 | ex6 | ex7 | ex8 | ex9 | ex10 |
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Machine Learning A-Z™: Hands-On Python & R In Data Science | Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. | 4.5 | 163547 | 904273 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 43h 4m total length | https://www.udemy.com/course/machinelearning/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: Part 1 - Data Preprocessing Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 - Clustering: K-Means, Hierarchical Clustering Part 5 - Association Rule Learning: Apriori, Eclat Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects. Important updates (June 2020): CODES ALL UP TO DATE DEEP LEARNING CODED IN TENSORFLOW 2.0 TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST! | https://www.udemy.com/course/machinelearning/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Machine Learning | Data Scientist | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Python for Data Science and Machine Learning Bootcamp | Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more! | Bestseller | 4.6 | 123237 | 593726 | Created by Jose Portilla | May-20 | English | $9.99 | 24h 54m total length | https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visualizationsUse plotly for interactive visualizationsMachine Learning with SciKit Learn, including:Linear RegressionK Nearest NeighborsK Means ClusteringDecision TreesRandom ForestsNatural Language ProcessingNeural Nets and Deep LearningSupport Vector Machinesand much, much more! Enroll in the course and become a data scientist today! | https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
The Data Science Course 2022: Complete Data Science Bootcamp | Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning | Bestseller | 4.6 | 115290 | 544630 | Created by 365 Careers, 365 Careers Team | Oct-22 | English | $11.99 | 31h 51m total length | https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding of the data science field and the type of analysis carried out Mathematics Statistics Python Applying advanced statistical techniques in Python Data Visualization Machine Learning Deep Learning Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is. So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2022. We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). The Skills 1. Intro to Data and Data Science Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science. 2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on. Why learn it? Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. 3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist. Why learn it? This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. 4. Python Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning. Why learn it? When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language. 5. Tableau Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers. 6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail. Why learn it? Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section. 7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow. Why learn it? Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines. ***What you get*** A $1250 data science training program Active Q&A support All the knowledge to get hired as a data scientist A community of data science learners A certificate of completion Access to future updates Solve real-life business cases that will get you the job You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and become a part of our data scientist program today. | https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Misc | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
R Programming A-Z™: R For Data Science With Real Exercises! | Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2 | Bestseller | 4.6 | 48435 | 243706 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 10h 35m total length | https://www.udemy.com/course/r-programming/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Learn R Programming by doing! There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, What you will learn: Learn how to use R Studio Learn the core principles of programming Learn how to create vectors in R Learn how to create variables Learn about integer, double, logical, character, and other types in R Learn how to create a while() loop and a for() loop in R Learn how to build and use matrices in R Learn the matrix() function, learn rbind() and cbind() Learn how to install packages in R Sincerely, Kirill Eremenko | https://www.udemy.com/course/r-programming/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | >=45K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Deep Learning A-Z™: Hands-On Artificial Neural Networks | Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. | Bestseller | 4.5 | 41467 | 343771 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 22h 32m total length | https://www.udemy.com/course/deeplearning/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | *** As seen on Kickstarter ***Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. --- Why Deep Learning A-Z? --- Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there: 1. ROBUST STRUCTURE The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning. 2. INTUITION TUTORIALS So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms. With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer. 3. EXCITING PROJECTS Are you tired of courses based on over-used, outdated data sets? Yes? Well then you're in for a treat. Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges: Artificial Neural Networks to solve a Customer Churn problem Convolutional Neural Networks for Image Recognition Recurrent Neural Networks to predict Stock Prices Self-Organizing Maps to investigate Fraud Boltzmann Machines to create a Recomender System Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth. 4. HANDS-ON CODING In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after. This is a course which naturally extends into your career. 5. IN-COURSE SUPPORT Have you ever taken a course or read a book where you have questions but cannot reach the author? Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help. In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum. No matter how complex your query, we will be there. The bottom line is we want you to succeed. --- The Tools --- Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both! TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more. PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook. So which is better and for what? Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances. The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques. --- More Tools --- Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it. Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing. --- Even More Tools --- Scikit-learn the most practical Machine Learning library. We will mainly use it: to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation to improve our models with effective Parameter Tuning to preprocess our data, so that our models can learn in the best conditions And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience. Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently. --- Who Is This Course For? --- As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology. If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident. If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications. --- Real-World Case Studies --- Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges: #1 Churn Modelling Problem In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank. Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach. If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn. #2 Image Recognition In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder. For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog! #3 Stock Price Prediction In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans. The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course! In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them. #4 Fraud Detection According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course. This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card. This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications. #5 & 6 Recommender Systems From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models. Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of. And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix! --- Summary --- In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies. We are super enthusiastic about Deep Learning and hope to see you inside the class! Kirill & Hadelin | https://www.udemy.com/course/deeplearning/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Deep Learning | Data Scientist | >=4 | >=40K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Statistics for Data Science and Business Analysis | Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis | Bestseller | 4.6 | 34795 | 159063 | Created by 365 Careers, 365 Careers Team | Jan-21 | English | $9.99 | 4h 51m total length | https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? And you want to acquire the quantitative skills needed for the job? Well then, you’ve come to the right place! Statistics for Data Science and Business Analysis is here for you! (with TEMPLATES in Excel included) This is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is: Easy to understand Comprehensive Practical To the point Packed with plenty of exercises and resources Data-driven Introduces you to the statistical scientific lingo Teaches you about data visualization Shows you the main pillars of quant research It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction. Teaching is our passion We worked full-time for several months to create the best possible Statistics course, which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing. What makes this course different from the rest of the Statistics courses out there? High-quality production – HD video and animations (This isn’t a collection of boring lectures!) Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level) Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist Extensive Case Studies that will help you reinforce everything you’ve learned Excellent support - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day Dynamic - we don’t want to waste your time! The instructor sets a very good pace throughout the whole course Why do you need these skills? Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated Growth - this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you. Click 'Buy now' and let's start learning together today! | https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Business Analyst | Yes | >=4 | >=30K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Data Science A-Z™: Real-Life Data Science Exercises Included | Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more! | 4.6 | 32461 | 207773 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 21h 12m total length | https://www.udemy.com/course/datascience/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Extremely Hands-On... Incredibly Practical... Unbelievably Real! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end. In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it! This course will give you a full overview of the Data Science journey. Upon completing this course you will know: How to clean and prepare your data for analysis How to perform basic visualisation of your data How to model your data How to curve-fit your data And finally, how to present your findings and wow the audience This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools: SQL SSIS Tableau Gretl This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need. Or you can do the whole course and set yourself up for an incredible career in Data Science. The choice is yours. Join the class and start learning today! See you inside, Sincerely, Kirill Eremenko | https://www.udemy.com/course/datascience/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | >=30K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Machine Learning, Data Science and Deep Learning with Python | Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks | 4.5 | 28130 | 169621 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Sep-22 | English | $9.99 | 15h 36m total length | https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 137161 | 661038 | New! Updated with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the A-Z of machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD | https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/#instructor-1 | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Machine Learning | Founder/Entrepreneur | >=4 | >=25K | >=1 Lakh | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Python A-Z™: Python For Data Science With Real Exercises! | Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization | 4.6 | 25159 | 148807 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 11h 5m total length | https://www.udemy.com/course/python-coding/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Learn Python Programming by doing! There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, What you will learn: Learn the core principles of programming Learn how to create variables How to visualize data in Seaborn How to create histograms, KDE plots, violin plots and style your charts to perfection Learn about integer, float, logical, string and other types in Python Learn how to create a while() loop and a for() loop in Python And much more.... Sincerely, Kirill Eremenko | https://www.udemy.com/course/python-coding/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Python | Data Scientist | >=4 | >=25K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Artificial Intelligence A-Z™: Learn How To Build An AI | Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! | Bestseller | 4.4 | 21826 | 188327 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Luka Anicin, Ligency Team, Jordan Sauchuk | Nov-22 | English | $15.99 | 16h 57m total length | https://www.udemy.com/course/artificial-intelligence-az/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1623570 | *** AS SEEN ON KICKSTARTER ***Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering: How to start building AI with no previous coding experience using PythonHow to merge AI with OpenAI Gym to learn as effectively as possibleHow to optimize your AI to reach its maximum potential in the real world Here is what you will get with this course: 1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. 2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited. 3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line. 4. Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect. 5. In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum. | https://www.udemy.com/course/artificial-intelligence-az/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Artificial Intelligence | Founder/Entrepreneur | >=4 | >=20K | >=1 Lakh | >=4 | >=2.5 Lakh | >=10 Lakh | ||||||||||||||||
Spark and Python for Big Data with PySpark | Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more! | Bestseller | 4.6 | 20142 | 108512 | Created by Jose Portilla | May-20 | English | $15.99 | 10h 35m total length | https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems! Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market! This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem! We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion! If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you! | https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Big Data/Data Engineer | Head/Director | >=4 | >=20K | >=1 Lakh | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Data Analysis with Pandas and Python | Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome! | Bestseller | 4.6 | 18200 | 173961 | Created by Boris Paskhaver | Jul-22 | English | $10.99 | 22h 0m total length | https://www.udemy.com/course/data-analysis-with-pandas/ | Boris Paskhaver | Software Engineer | Consultant | Author | 4.6 | 34791 | 343544 | Student Testimonials: The instructor knows the material, and has detailed explanation on every topic he discusses. Has clarity too, and warns students of potential pitfalls. He has a very logical explanation, and it is easy to follow him. I highly recommend this class, and would look into taking a new class from him. - Diana This is excellent, and I cannot complement the instructor enough. Extremely clear, relevant, and high quality - with helpful practical tips and advice. Would recommend this to anyone wanting to learn pandas. Lessons are well constructed. I'm actually surprised at how well done this is. I don't give many 5 stars, but this has earned it so far. - Michael This course is very thorough, clear, and well thought out. This is the best Udemy course I have taken thus far. (This is my third course.) The instruction is excellent! - James Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include: installing sorting filtering grouping aggregating de-duplicating pivoting munging deleting merging visualizing and more! Why learn pandas? If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you! Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language. Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it "Excel on steroids"! Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package. Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas! Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today! | https://www.udemy.com/course/data-analysis-with-pandas/#instructor-1 | Hi there, it's nice to meet you! I'm a New York City-based software engineer, author, and consultant who's been teaching on Udemy since 2016. Like many of my peers, I did not follow a conventional approach to my current role as a web developer. After graduating from New York University in 2013 with a degree in Business Economics and Marketing, I worked as a business analyst, systems administrator, and data analyst for a variety of companies including a digital marketing agency, a financial services firm, and an international tech powerhouse. At one of those roles, I was fortunate enough to be challenged to build several projects with Python and JavaScript. There was no formal computer science education for me; I discovered coding entirely by accident. A small work interest quickly blossomed into a passionate weekend hobby. Eventually, I left my former role to complete App Academy, a rigorous full-stack web development bootcamp in NYC. The rest is history. I've always been fascinated by the intersection of technology and education, especially since I've struggled with many of the traditional resources people use to learn how to program. My goal as an instructor is to create comprehensive step-by-step courses that break down the complex details into small, digestible pieces. I like to build the kind of material that I myself would have loved to have when I was starting out. I'm passionate about teaching and would love to help you discover what code can do for you. See you in a course soon! | Python | Consultant | >=4 | >=15K | >=1 Lakh | >=4 | Below 1 Lakh | >=3 Lakh | ||||||||||||||||
Complete Guide to TensorFlow for Deep Learning with Python | Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! | 4.2 | 16556 | 93224 | Created by Jose Portilla | Apr-20 | English | $14.99 | 14h 9m total length | https://www.udemy.com/course/complete-guide-to-tensorflow-for-deep-learning-with-python/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks AutoEncoders Reinforcement Learning OpenAI Gym and much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google! Become a machine learning guru today! We'll see you inside the course! | https://www.udemy.com/course/complete-guide-to-tensorflow-for-deep-learning-with-python/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Deep Learning | Head/Director | >=4 | >=15K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Apache Spark with Scala - Hands On with Big Data! | Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets, on your desktop or on Hadoop with Scala! | 4.5 | 16302 | 87707 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Sep-22 | English | $10.99 | 8h 58m total length | https://www.udemy.com/course/apache-spark-with-scala-hands-on-with-big-data/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 137161 | 661038 | New! Completely updated and re-recorded for Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API. “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think, and you'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Spark works best when using the Scala programming language, and this course includes a crash-course in Scala to get you up to speed quickly. For those more familiar with Python however, a Python version of this class is also available: "Taming Big Data with Apache Spark and Python - Hands On". Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course. Learn the concepts of Spark's Resilient Distributed Datasets, DataFrames, and Datasets. Get a crash course in the Scala programming language Develop and run Spark jobs quickly using Scala, IntelliJ, and SBT Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Practice using other Spark technologies, like Spark SQL, DataFrames, DataSets, Spark Streaming, Machine Learning, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to SpiderMan? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. over 8 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Enroll now, and enjoy the course! "I studied Spark for the first time using Frank's course "Apache Spark 2 with Scala - Hands On with Big Data!". It was a great starting point for me, gaining knowledge in Scala and most importantly practical examples of Spark applications. It gave me an understanding of all the relevant Spark core concepts, RDDs, Dataframes & Datasets, Spark Streaming, AWS EMR. Within a few months of completion, I used the knowledge gained from the course to propose in my current company to work primarily on Spark applications. Since then I have continued to work with Spark. I would highly recommend any of Franks courses as he simplifies concepts well and his teaching manner is easy to follow and continue with! " - Joey Faherty | https://www.udemy.com/course/apache-spark-with-scala-hands-on-with-big-data/#instructor-1 | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Big Data/Data Engineer | Founder/Entrepreneur | >=4 | >=15K | >=50K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Data Science and Machine Learning Bootcamp with R | Learn how to use the R programming language for data science and machine learning and data visualization! | 4.7 | 15500 | 85747 | Created by Jose Portilla | Dec-20 | English | $9.99 | 17h 45m total length | https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning: Programming with R Advanced R Features Using R Data Frames to solve complex tasks Use R to handle Excel Files Web scraping with R Connect R to SQL Use ggplot2 for data visualizations Use plotly for interactive visualizations Machine Learning with R, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Data Mining Twitter Neural Nets and Deep Learning Support Vectore Machines and much, much more! Enroll in the course and become a data scientist today! | https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | >=15K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Taming Big Data with Apache Spark and Python - Hands On! | PySpark tutorial with 20+ hands-on examples of analyzing large data sets on your desktop or on Hadoop with Python! | Bestseller | 4.5 | 13924 | 83336 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Oct-22 | English | $10.99 | 6h 57m total length | https://www.udemy.com/course/taming-big-data-with-apache-spark-hands-on/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 137161 | 661038 | New! Updated for Spark 3, more hands-on exercises, and a stronger focus on DataFrames and Structured Streaming. “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark and specifically PySpark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think. Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of Spark's DataFrames and Resilient Distributed Datastores Develop and run Spark jobs quickly using Python and pyspark Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. This course uses the familiar Python programming language; if you'd rather use Scala to get the best performance out of Spark, see my "Apache Spark with Scala - Hands On with Big Data" course instead. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. 7 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Wrangling big data with Apache Spark is an important skill in today's technical world. Enroll now! " I studied "Taming Big Data with Apache Spark and Python" with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course! " - Cleuton Sampaio De Melo Jr. | https://www.udemy.com/course/taming-big-data-with-apache-spark-hands-on/#instructor-1 | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Big Data/Data Engineer | Founder/Entrepreneur | >=4 | >=10K | >=50K | >=4 | >=1 Lakh | >=5 Lakh | ||||||||||||||||
Complete Machine Learning & Data Science Bootcamp 2023 | Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! | 4.6 | 13091 | 78863 | Created by Andrei Neagoie, Daniel Bourke, Zero To Mastery | Nov-22 | English | $9.99 | 43h 48m total length | https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/ | Andrei Neagoie | Founder of zerotomastery.io | 4.6 | 233000 | 906458 | This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery! Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know). This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want. The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)! The topics covered in this course are: - Data Exploration and Visualizations - Neural Networks and Deep Learning - Model Evaluation and Analysis - Python 3 - Tensorflow 2.0 - Numpy - Scikit-Learn - Data Science and Machine Learning Projects and Workflows - Data Visualization in Python with MatPlotLib and Seaborn - Transfer Learning - Image recognition and classification - Train/Test and cross validation - Supervised Learning: Classification, Regression and Time Series - Decision Trees and Random Forests - Ensemble Learning - Hyperparameter Tuning - Using Pandas Data Frames to solve complex tasks - Use Pandas to handle CSV Files - Deep Learning / Neural Networks with TensorFlow 2.0 and Keras - Using Kaggle and entering Machine Learning competitions - How to present your findings and impress your boss - How to clean and prepare your data for analysis - K Nearest Neighbours - Support Vector Machines - Regression analysis (Linear Regression/Polynomial Regression) - How Hadoop, Apache Spark, Kafka, and Apache Flink are used - Setting up your environment with Conda, MiniConda, and Jupyter Notebooks - Using GPUs with Google Colab By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others. Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems. Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows. Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career. You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean! Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course! Taught By: Daniel Bourke: A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages. My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen. I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more. Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups. Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims. My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?". Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views. I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible. My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know. Questions are always welcome. -------- Andrei Neagoie: Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the course! | https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/#instructor-1 | Andrei is the instructor of some of the highest rated programming and technical courses online. He is now the founder of ZTM Academy which is one of the fastest growing education platforms in the world. ZTM Academy is known for having some of the best instructors and success rates for students. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Tesla, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the courses! | Machine Learning | Founder/Entrepreneur | >=4 | >=10K | >=50K | >=4 | >=2 Lakh | >=5 Lakh | |||||||||||||||||
Introduction to Machine Learning for Data Science | A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist. | 4.4 | 12294 | 57392 | Created by David Valentine | Jul-20 | English | $9.99 | 5h 33m total length | https://www.udemy.com/course/machine-learning-for-data-science/ | David Valentine | The Backyard Data Scientist | 4.4 | 12420 | 84910 | Course Most Recently Updated Nov/2018! Thank you all for the huge response to this emerging course! We are delighted to have over 20,000 students in over 160 different countries. I'm genuinely touched by the overwhelmingly positive and thoughtful reviews. It's such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. I'm also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)... I've got you covered. Most importantly: To make this course "real", we've expanded. In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections! We hope you enjoy the new content! Unlock the secrets of understanding Machine Learning for Data Science! In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come. Our exotic journey will include the core concepts of: The train wreck definition of computer science and one that will actually instead make sense. An explanation of data that will have you seeing data everywhere that you look! One of the “greatest lies” ever sold about the future computer science. A genuine explanation of Big Data, and how to avoid falling into the marketing hype. What is Artificial intelligence? Can a computer actually think? How do computers do things like navigate like a GPS or play games anyway? What is Machine Learning? And if a computer can think – can it learn? What is Data Science, and how it relates to magical unicorns! How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another. We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now. We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014. Do you have a super computer in your home? You might be surprised to learn the truth. We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense. Most importantly we’ll show how this is changing our lives. Not just the lives of business leaders, but most importantly…you too! To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process: How do you solve problems with Machine Learning and what are five things you must do to be successful? How to ask the right question, to be solved by Machine Learning. Identifying, obtaining and preparing the right data … and dealing with dirty data! How every mess is “unique” but that tidy data is like families! How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers” And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science. Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. We’ll explore: How to start applying Machine Learning without losing your mind. What equipment Data Scientists use, (the answer might surprise you!) The top five tools Used for data science, including some surprising ones. And for each of the top five tools – we’ll explain what they are, and how to get started using them. And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems. Bonus Course! To make this “really real”, I’ve included a bonus course! Most importantly in the bonus course I’ll include information at the end of every section titled “Further Magic to Explore” which will help you to continue your learning experience. In this bonus course we’ll explore: Creating a real live Machine Learning Example of Titanic proportions. That’s right – we are going to predict survivability onboard the Titanic! Use Anaconda Jupyter and python 3.x A crash course in python - covering all the core concepts of Python you need to make sense of code examples that follow. See the included free cheat sheet! Hands on running Python! (Interactively, with scripts, and with Jupyter) Basics of how to use Jupyter Notebooks Reviewing and reinforcing core concepts of Machine Learning (that we’ll soon apply!) Foundations of essential Machine Learning and Data Science modules: NumPy – An Array Implementation Pandas – The Python Data Analysis Library Matplotlib – A plotting library which produces quality figures in a variety of formats SciPy – The fundamental Package for scientific computing in Python Scikit-Learn – Simple and efficient tools data mining, data analysis, and Machine Learning In the titanic hands on example we’ll follow all the steps of the Machine Learning workflow throughout: 1. Asking the right question. 2. Identifying, obtaining, and preparing the right data 3. Identifying and applying a Machine Learning algorithm 4. Evaluating the performance of the model and adjusting 5. Using and presenting the model We’ll also see a real world example of problems in Machine learning, including underfit and overfit. The bonus course finishes with a conclusion and further resources to continue your Machine Learning journey. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you! Sign up right now, and we'll see you – on the other side! | https://www.udemy.com/course/machine-learning-for-data-science/#instructor-1 | Mr. David Valentine is an decorated Enterprise Architect with over seventeen years of experience in enterprise computing environments. He currently works for the Province of Manitoba, in Canada where he is a responsible for the architecture of the Server and Mainframe compute environment. Mr. Valentine has a passion for Data Science, Computer Science, Machine Learning and Data Science. As the "Backyard Data Scientist", he bring his experience and ability to simplify challenging technical topics to Data Science. He's delighted to offer the world his first course on the Udemy platform, "Machine Learning for Data Science" | Machine Learning | Data Scientist | >=4 | >=10K | >=50K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
NLP - Natural Language Processing with Python | Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing | 4.6 | 12274 | 63068 | Created by Jose Portilla | Sep-19 | English | $9.99 | 11h 24m total length | https://www.udemy.com/course/nlp-natural-language-processing-with-python/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text. We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more! Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems. We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information. Through state of the art visualization libraries we will be able view these relationships in real time. Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages. We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files. This course even covers advanced topics, such as sentiment analysis of text with the NLTK library, and creating semantic word vectors with the Word2Vec algorithm. Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots! Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants. All of this comes with a 30 day money back garuantee, so you can try the course risk free. What are you waiting for? Become an expert in natural language processing today! I will see you inside the course, Jose | https://www.udemy.com/course/nlp-natural-language-processing-with-python/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | NLP | Head/Director | >=4 | >=10K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Data Science: Natural Language Processing (NLP) in Python | Applications: decrypting ciphers, spam detection, sentiment analysis, article spinners, and latent semantic analysis. | 4.6 | 11263 | 43097 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 11h 50m total length | https://www.udemy.com/course/data-science-natural-language-processing-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE. After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms. The second project, where we begin to use more traditional "machine learning", is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these. Next we'll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market. We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA. Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to give you ideas for how you might improve on them yourself. Once mastered, you can use it as an SEO, or search engine optimization tool. Internet marketers everywhere will love you if you can do this for them! This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... | https://www.udemy.com/course/data-science-natural-language-processing-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | NLP | Engineer/Developer | >=4 | >=10K | >=40K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Statistics for Business Analytics and Data Science A-Z™ | Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More! | 4.5 | 10222 | 57186 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $11.99 | 6h 2m total length | https://www.udemy.com/course/data-statistics/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | If you are aiming for a career as a Data Scientist or Business Analyst then brushing up on your statistics skills is something you need to do. But it's just hard to get started... Learning / re-learning ALL of stats just seems like a daunting task. That's exactly why I have created this course! Here you will quickly get the absolutely essential stats knowledge for a Data Scientist or Analyst. This is not just another boring course on stats. This course is very practical. I have specifically included real-world examples of business challenges to show you how you could apply this knowledge to boost YOUR career. At the same time you will master topics such as distributions, the z-test, the Central Limit Theorem, hypothesis testing, confidence intervals, statistical significance and many more! So what are you waiting for? Enroll now and empower your career! | https://www.udemy.com/course/data-statistics/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Business Analyst | Data Scientist | Yes | >=4 | >=10K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
2022 Python for Machine Learning & Data Science Masterclass | Learn about Data Science and Machine Learning with Python! Including Numpy, Pandas, Matplotlib, Scikit-Learn and more! | 4.6 | 10093 | 77765 | Created by Jose Portilla | Sep-21 | English | $9.99 | 44h 4m total length | https://www.udemy.com/course/python-for-machine-learning-data-science-masterclass/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | This is the most complete course online for learning about Python, Data Science, and Machine Learning. Join Jose Portilla's over 2.6 million students to learn about the future today! What is in the course? Welcome to the most complete course on learning Data Science and Machine Learning on the internet! After teaching over 2 million students I've worked for over a year to put together what I believe to be the best way to go from zero to hero for data science and machine learning in Python! This course is designed for the student who already knows some Python and is ready to dive deeper into using those Python skills for Data Science and Machine Learning. The typical starting salary for a data scientists can be over $150,000 dollars, and we've created this course to help guide students to learning a set of skills to make them extremely hirable in today's workplace environment. We'll cover everything you need to know for the full data science and machine learning tech stack required at the world's top companies. Our students have gotten jobs at McKinsey, Facebook, Amazon, Google, Apple, Asana, and other top tech companies! We've structured the course using our experience teaching both online and in-person to deliver a clear and structured approach that will guide you through understanding not just how to use data science and machine learning libraries, but why we use them. This course is balanced between practical real world case studies and mathematical theory behind the machine learning algorithms. We cover advanced machine learning algorithms that most other courses don't! Including advanced regularization methods and state of the art unsupervised learning methods, such as DBSCAN. This comprehensive course is designed to be on par with Bootcamps that usually cost thousands of dollars and includes the following topics: Programming with Python NumPy with Python Deep dive into Pandas for Data Analysis Full understanding of Matplotlib Programming Library Deep dive into seaborn for data visualizations Machine Learning with SciKit Learn, including: Linear Regression Regularization Lasso Regression Ridge Regression Elastic Net K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Support Vector Machines Hierarchal Clustering DBSCAN PCA Model Deployment and much, much more! As always, we're grateful for the chance to teach you data science, machine learning, and python and hope you will join us inside the course to boost your skillset! -Jose and Pierian Data Inc. Team | https://www.udemy.com/course/python-for-machine-learning-data-science-masterclass/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | >=10K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Artificial Intelligence: Reinforcement Learning in Python | Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications | Bestseller | 4.7 | 9333 | 42920 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $79.99 | 14h 43m total length | https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible. In 2016 we saw Google’s AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario. Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance. If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially. Learning about supervised and unsupervised machine learning is no small feat. To date I have over TWENTY FIVE (25!) courses just on those topics alone. And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is very from both supervised and unsupervised learning. It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true artificial general intelligence. What’s covered in this course? The multi-armed bandit problem and the explore-exploit dilemma Ways to calculate means and moving averages and their relationship to stochastic gradient descent Markov Decision Processes (MDPs) Dynamic Programming Monte Carlo Temporal Difference (TD) Learning (Q-Learning and SARSA) Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm) How to use OpenAI Gym, with zero code changes Project: Apply Q-Learning to build a stock trading bot If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Calculus Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/artificial-intelligence-reinforcement-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Artificial Intelligence | Engineer/Developer | >=4 | Below 10K | >=40K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Data Science: Deep Learning and Neural Networks in Python | The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code | 4.7 | 8720 | 51141 | Created by Lazy Programmer Inc. | Nov-22 | English | $9.99 | 11h 16m total length | https://www.udemy.com/course/data-science-deep-learning-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow. I have other courses that cover more advanced topics, such as Convolutional Neural Networks, Restricted Boltzmann Machines, Autoencoders, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/data-science-deep-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=50K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
R Programming: Advanced Analytics In R For Data Science | Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2 | 4.6 | 8196 | 57632 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 5h 58m total length | https://www.udemy.com/course/r-analytics/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Ready to take your R Programming skills to the next level? Want to truly become proficient at Data Science and Analytics with R? This course is for you! Professional R Video training, unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD. In this course, you will learn: How to prepare data for analysis in R How to perform the median imputation method in R How to work with date-times in R What Lists are and how to use them What the Apply family of functions is How to use apply(), lapply() and sapply() instead of loops How to nest your own functions within apply-type functions How to nest apply(), lapply() and sapply() functions within each other And much, much more! The more you learn, the better you will get. After every module, you will have a robust set of skills to take with you into your Data Science career. We prepared real-life case studies. In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries. In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks. In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that. | https://www.udemy.com/course/r-analytics/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | Below 10K | >=50K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Tensorflow 2.0: Deep Learning and Artificial Intelligence | Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More! | 4.6 | 8055 | 39699 | Created by Lazy Programmer Inc., Lazy Programmer Team | Nov-22 | English | $15.99 | 22h 22m total length | https://www.udemy.com/course/deep-learning-tensorflow-2/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | Welcome to Tensorflow 2.0! What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Current projects include: Natural Language Processing (NLP) Recommender Systems Transfer Learning for Computer Vision Generative Adversarial Networks (GANs) Deep Reinforcement Learning Stock Trading Bot Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches). Advanced Tensorflow topics include: Deploying a model with Tensorflow Serving (Tensorflow in the cloud) Deploying a model with Tensorflow Lite (mobile and embedded applications) Distributed Tensorflow training with Distribution Strategies Writing your own custom Tensorflow model Converting Tensorflow 1.x code to Tensorflow 2.0 Constants, Variables, and Tensors Eager execution Gradient tape Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics. Thanks for reading, and I’ll see you in class! WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-learning-tensorflow-2/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Artificial Intelligence | Engineer/Developer | >=4 | Below 10K | >=35K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Interactive Python Dashboards with Plotly and Dash | Learn how to create interactive plots and intelligent dashboards with Plotly, Python, and the Dash library! | 4.6 | 7909 | 42811 | Created by Jose Portilla | Sep-19 | English | $15.99 | 9h 43m total length | https://www.udemy.com/course/interactive-python-dashboards-with-plotly-and-dash/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to Python Visualization Dashboards with Plotly's Dash Library! This course will teach your everything you need to know to use Python to create interactive dashboard's with Plotly's new Dash library! Have you ever wanted to take your Python skills to the next level in data visualization? With this course you will be able to create fully customization, interactive dashboards with the open source libraries of Plotly and Dash. Dash instructional courses from Plotly usually cost more than $1000, but now you can get the bootcamp experience for a fraction of that price in this self-paced course that includes example code, explanatory videos, student support in our chat channels, Question and Answer Forums, and interactive exercises. We'll start off by teaching you enough Numpy and Pandas that you feel comfortable working and generating data in our quick crash course. Then we'll continue by teaching you about basic data visualization with Plotly, including scatter plots, line charts, bar charts, bubble charts, box plots, histograms, distribution plots, heat maps, and more! We'll also give you an intuition of when to use each plot type. After this and at the end of each section you'll be given exercise tasks to test and evaluate your new skills, a feature no other Plotly Dash training offers! Once you have a grasp on Plotly basics we'll move on to the bulk of the course which is utilizing the Dash library to leverage the power of plotly plots to create interactive dashboards. We'll discuss how to create layouts for dashboards, how to have interactive callbacks, dealing with multiple inputs and outputs, creating interactive components, and more! We'll finish off the course by going over live updating dashboards that automatically update in real time and even show you how you can deploy your dashboards live to the web with the Heroku service. By taking this course you will be learning the bleeding edge of data visualization technology with Python and gain a valuable new skill to show your colleagues or potential employers. After completing the course you will have a certification you can post to your LinkedIn profile and a portfolio of dashboard projects you can share as well. All of this comes with a 30 day money back guarantee, so what are you waiting for? Enroll today and we'll see you inside the course! | https://www.udemy.com/course/interactive-python-dashboards-with-plotly-and-dash/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Python | Head/Director | >=4 | Below 10K | >=40K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
DP-900 Azure Data Fundamentals Exam Prep In One Day AUG 2022 | Learn the basics of Azure database services and get certified with this complete DP-900 course! | Bestseller | 4.5 | 7340 | 38045 | Created by Scott Duffy • 800.000+ Students, Software Architect.ca | Nov-22 | English | $9.99 | 3h 21m total length | https://www.udemy.com/course/dp900-azure/ | Scott Duffy • 800.000+ Students | Bestselling Azure & TOGAF® Trainer, Microsoft Azure MVP | 4.5 | 230424 | 839399 | LEARN AZURE DATABASE AND DATA PROCESSING TECHNOLOGIES IN ONE DAY! The course is completely up-to-date with new requirements. A brand new course, just launched! Complete preparation for the new DP-900 Azure Data Fundamentals exam. The content of this exam was updated on August 4, 2022. Describe core data concepts (15-20%) Describe how to work with relational data on Azure (25-30%) Describe how to work with non-relational data on Azure (25-30%) Describe an analytics workload on Azure (25-30%) This brand-new course completely covers the DP-900 exam from start to finish. Always updated with the latest requirements. This course goes over each requirement of the exam in detail. If you have no background in databases and want to learn about them and want to learn more about database concepts and services within Azure, or have some background in databases and want to progress eventually to an Azure Data Engineer or Data Analyst type role, this course is a great resource for you. Microsoft Azure is still the fastest-growing large cloud platform. The opportunities for jobs in cloud computing are still out there, and finding well-qualified people is the #1 problem that businesses have. If you're looking to change your career, this would be a good entry point into cloud computing on the data side. Sign up today! Added English, Spanish, and Portuguese closed captions. | https://www.udemy.com/course/dp900-azure/#instructor-1 | Hi there, my name is Scott Duffy. And I love making complex technical topics easy to understand. This has been the basis of my entire career – as a developer, as a development manager, as a software architect – over the past 20 years. I spend half my time in the world of business, explaining complex technical topics to business owners and stakeholders so that they can understand and agree with my approach to solving their business problems with technical solutions. And the other half with developers, explaining the business reasons behind decisions and ensuring that any decisions made on the technical side don't restrict the business in unexpected ways. And I'm here on Udemy to teach what I know in an approachable way. I started teaching courses in 2014, and have taught over 800,000 students. I am grateful every day for being able to connect with so many students in almost every country around the world. I'm a certified Enterprise Architect and certified cloud architect. I have been developing with Microsoft technologies for 20 years, starting with Classic ASP, and all the versions of .NET. We now live in the cloud era, with Microsoft Azure being prevalent in most large enterprises. I'm certified as an Azure Architect and Developer too. As well as AWS Solution Architect Associate (SAA). Microsoft MVP Award winner for 2022. | Azure | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=35K | >=4 | >=2 Lakh | >=5 Lakh | ||||||||||||||||
Natural Language Processing with Deep Learning in Python | Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets | 4.6 | 7121 | 42309 | Created by Lazy Programmer Inc. | Nov-22 | English | $9.99 | 12h 2m total length | https://www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | In this course we are going to look at NLP (natural language processing) with deep learning. Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices. These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words. In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course. First up is word2vec. In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know. Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like: king - man = queen - woman France - Paris = England - London December - Novemeber = July - June For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers. We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems. Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train. We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity. Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words. All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix addition, multiplication probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own Can write a feedforward neural network in Theano or TensorFlow Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function Helpful to have experience with tree algorithms WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/natural-language-processing-with-deep-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=40K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Complete Tensorflow 2 and Keras Deep Learning Bootcamp | Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras! | 4.6 | 6774 | 40746 | Created by Jose Portilla | Jun-22 | English | $11.99 | 19h 12m total length | https://www.udemy.com/course/complete-tensorflow-2-and-keras-deep-learning-bootcamp/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand. We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including NumPy Crash Course Pandas Data Analysis Crash Course Data Visualization Crash Course Neural Network Basics TensorFlow Basics Keras Syntax Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks AutoEncoders GANs - Generative Adversarial Networks Deploying TensorFlow into Production and much more! Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines. TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google! Become a deep learning guru today! We'll see you inside the course! | https://www.udemy.com/course/complete-tensorflow-2-and-keras-deep-learning-bootcamp/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Deep Learning | Head/Director | >=4 | Below 10K | >=40K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Deep Learning and Computer Vision A-Z™: OpenCV, SSD & GANs | Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps. | 4.1 | 6348 | 45759 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 11h 5m total length | https://www.udemy.com/course/computer-vision-a-z/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1623570 | *** AS SEEN ON KICKSTARTER *** You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer. But what if you could also become a creator? What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place? Sounds too good to be true, doesn't it? But there actually is a way.. Computer Vision is by far the easiest way of becoming a creator. And it's not only the easiest way, it's also the branch of AI where there is the most to create. Why? You'll ask. That's because Computer Vision is applied everywhere. From health to retail to entertainment - the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially. Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human? And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of. So now that raises the question: how do you break into the World of Computer Vision? Up until now, computer vision has for the most part been a maze. A growing maze. As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost. On top of that, not only do you need to know how to use it - you also need to know how it works to maximise the advantage of using Computer Vision. To this problem we want to bring... Computer Vision A-Z. With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice! Can't wait to see you inside the class, Kirill & Hadelin | https://www.udemy.com/course/computer-vision-a-z/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Computer Vision | Founder/Entrepreneur | >=4 | Below 10K | >=45K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
Bayesian Machine Learning in Python: A/B Testing | Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More | Bestseller | 4.6 | 5763 | 32468 | Created by Lazy Programmer Inc. | Nov-22 | English | $9.99 | 10h 24m total length | https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | This course is all about A/B testing. A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more. A/B testing is all about comparing things. If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions. In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things. First, we’ll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma. You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning. We’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1. Finally, we’ll improve on both of those by using a fully Bayesian approach. Why is the Bayesian method interesting to us in machine learning? It’s an entirely different way of thinking about probability. It’s a paradigm shift. You’ll probably need to come back to this course several times before it fully sinks in. It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”. In sum - it’s going to give us a lot of powerful new tools that we can use in machine learning. The things you’ll learn in this course are not only applicable to A/B testing, but rather, we’re using A/B testing as a concrete example of how Bayesian techniques can be applied. You’ll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF) Python coding: if/else, loops, lists, dicts, sets Numpy, Scipy, Matplotlib WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | ||||||||||||||||
Probability and Statistics for Business and Data Science | Learn how to apply probability and statistics to real data science and business applications! | 4.6 | 5669 | 29712 | Created by Jose Portilla | Sep-19 | English | $11.99 | 5h 14m total length | https://www.udemy.com/course/probability-and-statistics-for-business-and-data-science/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to Probability and Statistics for Business and Data Science! In this course we cover what you need to know about probability and statistics to succeed in business and the data science field! This practical course will go over theory and implementation of statistics to real world problems. Each section has example problems, in course quizzes, and assessment tests. We’ll start by talking about the basics of data, understanding how to examine it with measurements of central tendency, dispersion, and also building an understanding of how bivariate data sources can relate to each other. Afterwards we’ll dive into probability , learning about combinations and permutations, as well as conditional probability and how to apply bayes theorem. Then we’ll move on to discussing the most common distributions found in statistics, creating a solid foundation of understanding how to work with uniform, binomial, poisson, and normal distributions. Up next we’ll talk about statistics, applying what we’ve learned so far to real world business cases, including hypothesis testing and the student's T distribution. We’ll end the course with 3 sections on advanced topics, such as ANOVA (analysis of variance), understanding regression analysis, and finally performing chi squared analysis. The sections are modular and organized by topic, so you can reference what you need and jump right in! Our course includes HD Video with clear explanations and high quality animations, we also include extensive case studies to show you how to apply this knowledge to the real world. We'll cover everything you need to know about statistics and probability to clearly tackle real world business and data science problems! Including: Measurements of Data Mean, Median, and Mode Variance and Standard Deviation Co-variance and Correlation Permutations and Combinations Unions and Intersections Conditional Probability Bayes Theorem Binomial Distribution Poisson Distribution Normal Distribution Sampling Central Limit Theorem Hypothesis Testing T-Distribution Testing Regression Analysis ANOVA Chi Squared and much more! Not only do you get great technical content, but you’ll also have access to our online QA forums as well as our student chat channel. Where the TAs and myself are happy to help out with any questions you encounter! Upon finishing this course you’ll receive a certificate of completion you can post on your linkedin profile to show off to your colleagues, or even potential employers! All of this content comes with a 30 day money back guarantee, so you can try out the course risk free! So what are you waiting for? Enroll today and we'll see you inside the course! | https://www.udemy.com/course/probability-and-statistics-for-business-and-data-science/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Statistics | Head/Director | Yes | >=4 | Below 10K | >=25K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
The Complete Machine Learning Course with Python | Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More! | 4.3 | 5566 | 32087 | Created by Codestars • over 2 million students worldwide!, Anthony NG, Rob Percival | Jan-20 | English | $11.99 | 17h 18m total length | https://www.udemy.com/course/machine-learning-course-with-python/ | Codestars • over 2 million students worldwide! | Teaching the Next Generation of Coders | 4.5 | 452936 | 2142912 | The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019! With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! The machine learning field is constantly evolving, and we want to make sure students have the most up-to-date information and practices available to them: Brand new sections include: Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extractions. And the following sections have all been improved and added to: All the codes have been updated to work with Python 3.6 and 3.7 The codes have been refactored to work with Google Colab Deep Learning and NLP Binary and multi-class classifications with deep learning Get the most up to date machine learning information possible, and get it in a single course! * * * The average salary of a Machine Learning Engineer in the US is $166,000! By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real life problems in your business, job or personal life with Machine Learning algorithms. Come learn Machine Learning with Python this exciting course with Anthony NG, a Senior Lecturer in Singapore who has followed Rob Percival’s “project based" teaching style to bring you this hands-on course. With over 18 hours of content and more than fifty 5 star ratings, it's already the longest and best rated Machine Learning course on Udemy! Build Powerful Machine Learning Models to Solve Any Problem You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen. By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more! Inside the course, you'll learn how to: Gain complete machine learning tool sets to tackle most real world problems Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them. Combine multiple models with by bagging, boosting or stacking Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data Develop in Jupyter (IPython) notebook, Spyder and various IDE Communicate visually and effectively with Matplotlib and Seaborn Engineer new features to improve algorithm predictions Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data Use SVM for handwriting recognition, and classification problems in general Use decision trees to predict staff attrition Apply the association rule to retail shopping datasets And much much more! No Machine Learning required. Although having some basic Python experience would be helpful, no prior Python knowledge is necessary as all the codes will be provided and the instructor will be going through them line-by-line and you get friendly support in the Q&A area. Make This Investment in Yourself If you want to ride the machine learning wave and enjoy the salaries that data scientists make, then this is the course for you! Take this course and become a machine learning engineer! | https://www.udemy.com/course/machine-learning-course-with-python/#instructor-1 | Best-selling Udemy instructor Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. But as just one man, Rob couldn’t create all the courses his students - more than half a million of them - wanted. That’s why Rob created Codestars. Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand. Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps. | Machine Learning | >=4 | Below 10K | >=30K | >=4 | >=4.5 Lakh | >=10 Lakh | ||||||||||||||||||
Deep Learning Prerequisites: Linear Regression in Python | Data science, machine learning, and artificial intelligence in Python for students and professionals | Bestseller | 4.6 | 5486 | 30298 | Created by Lazy Programmer Inc. | Nov-22 | English | $12.99 | 6h 21m total length | https://www.udemy.com/course/data-science-linear-regression-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of: deep learning machine learning data science statistics In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true. What's that you say? Moore's Law is not linear? You are correct! I will show you how linear regression can still be applied. In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs. We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight. Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE. If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/data-science-linear-regression-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | ||||||||||||||||
TensorFlow Developer Certificate in 2023: Zero to Mastery | Pass the TensorFlow Developer Certification Exam by Google. Become an AI, Machine Learning, and Deep Learning expert! | Bestseller | 4.7 | 5362 | 39813 | Created by Andrei Neagoie, Daniel Bourke, Zero To Mastery | Nov-22 | English | $9.99 | 63h 28m total length | https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery/ | Andrei Neagoie | Founder of zerotomastery.io | 4.6 | 233000 | 906458 | Just launched with all modern best practices for building neural networks with TensorFlow and passing the TensorFlow Developer Certificate exam! Join a live online community of over 900,000+ students and a course taught by a TensorFlow certified expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks and becoming part of Google's TensorFlow Certification Network. TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD according to 2023 statistics. By passing this certificate, which is officially recognized by Google, you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow developer! If you pass the exam, you will also be part of Google's TensorFlow Developer Network where recruiters are able to find you. The goal of this course is to teach you all the skills necessary for you to go and pass this exam and get your TensorFlow Certification from Google so you can display it on your resume, LinkedIn, Github and other social media platforms to truly make you stand out. Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!): This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. Most importantly, we will show you what the TensorFlow exam will look like for you. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 — TensorFlow Fundamentals Introduction to tensors (creating tensors) Getting information from tensors (tensor attributes) Manipulating tensors (tensor operations) Tensors and NumPy Using @tf.function (a way to speed up your regular Python functions) Using GPUs with TensorFlow 1 — Neural Network Regression with TensorFlow Build TensorFlow sequential models with multiple layers Prepare data for use with a machine learning model Learn the different components which make up a deep learning model (loss function, architecture, optimization function) Learn how to diagnose a regression problem (predicting a number) and build a neural network for it 2 — Neural Network Classification with TensorFlow Learn how to diagnose a classification problem (predicting whether something is one thing or another) Build, compile & train machine learning classification models using TensorFlow Build and train models for binary and multi-class classification Plot modelling performance metrics against each other Match input (training data shape) and output shapes (prediction data target) 3 — Computer Vision and Convolutional Neural Networks with TensorFlow Build convolutional neural networks with Conv2D and pooling layers Learn how to diagnose different kinds of computer vision problems Learn to how to build computer vision neural networks Learn how to use real-world images with your computer vision models 4 — Transfer Learning with TensorFlow Part 1: Feature Extraction Learn how to use pre-trained models to extract features from your own data Learn how to use TensorFlow Hub for pre-trained models Learn how to use TensorBoard to compare the performance of several different models 5 — Transfer Learning with TensorFlow Part 2: Fine-tuning Learn how to setup and run several machine learning experiments Learn how to use data augmentation to increase the diversity of your training data Learn how to fine-tune a pre-trained model to your own custom problem Learn how to use Callbacks to add functionality to your model during training 6 — Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini) Learn how to scale up an existing model Learn to how evaluate your machine learning models by finding the most wrong predictions Beat the original Food101 paper using only 10% of the data 7 — Milestone Project 1: Food Vision Combine everything you've learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper. 8 — NLP Fundamentals in TensorFlow Learn to: Preprocess natural language text to be used with a neural network Create word embeddings (numerical representations of text) with TensorFlow Build neural networks capable of binary and multi-class classification using: RNNs (recurrent neural networks) LSTMs (long short-term memory cells) GRUs (gated recurrent units) CNNs Learn how to evaluate your NLP models 9 — Milestone Project 2: SkimLit Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster) 10 — Time Series fundamentals in TensorFlow Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow) Prepare data for time series neural networks (features and labels) Understanding and using different time series evaluation methods MAE — mean absolute error Build time series forecasting models with TensorFlow RNNs (recurrent neural networks) CNNs (convolutional neural networks) 11 — Milestone Project 3: (Surprise) If you've read this far, you are probably interested in the course. This last project will be good.. we promise you, so see you inside the course 😉 TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers. We guarantee you this is the most comprehensive online course on passing the TensorFlow Developer Certificate to qualify you as a TensorFlow expert. So why wait? Make yourself stand out by becoming a Google Certified Developer and advance your career. See you inside the course! | https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery/#instructor-1 | Andrei is the instructor of some of the highest rated programming and technical courses online. He is now the founder of ZTM Academy which is one of the fastest growing education platforms in the world. ZTM Academy is known for having some of the best instructors and success rates for students. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Tesla, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the courses! | Tensor Flow | Founder/Entrepreneur | >=4 | Below 10K | >=35K | >=4 | >=2 Lakh | >=5 Lakh | ||||||||||||||||
Scala and Spark for Big Data and Machine Learning | Learn the latest Big Data technology - Spark and Scala, including Spark 2.0 DataFrames! | 4.5 | 5123 | 30177 | Created by Jose Portilla | Sep-19 | English | $9.99 | 10h 11m total length | https://www.udemy.com/course/scala-and-spark-for-big-data-and-machine-learning/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Learn how to utilize some of the most valuable tech skills on the market today, Scala and Spark! In this course we will show you how to use Scala and Spark to analyze Big Data. Scala and Spark are two of the most in demand skills right now, and with this course you can learn them quickly and easily! This course comes packed with content: Crash Course in Scala ProgrammingSpark and Big Data Ecosystem OverviewUsing Spark's MLlib for Machine Learning Scale up Spark jobs using Amazon Web ServicesLearn how to use Databrick's Big Data Platformand much more! This course comes with full projects for you including topics such as analyzing financial data or using machine learning to classify Ecommerce customer behavior! We teach the latest methodologies of Spark 2.0 so you can learn how to use SparkSQL, Spark DataFrames, and Spark's MLlib! After completing this course you will feel comfortable putting Scala and Spark on your resume! Thanks and I will see you inside the course! | https://www.udemy.com/course/scala-and-spark-for-big-data-and-machine-learning/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | Below 10K | >=30K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) | VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python | 4.6 | 5121 | 31289 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 15h 8m total length | https://www.udemy.com/course/advanced-computer-vision/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years. When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks. I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Let me give you a quick rundown of what this course is all about: We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!) We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots. In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label. You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time) We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors. Another very popular computer vision task that makes use of CNNs is called neural style transfer. This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds. I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images. Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system. I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class! AWESOME FACTS: One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs. Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math. Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Know how to build, train, and use a CNN using some library (preferably in Python) Understand basic theoretical concepts behind convolution and neural networks Decent Python coding skills, preferably in data science and the Numpy Stack WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/advanced-computer-vision/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Computer Vision | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Machine Learning & Deep Learning in Python & R | Covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R | 4.4 | 5008 | 358107 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 33h 4m total length | https://www.udemy.com/course/data_science_a_to_z/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right? You've found the right Machine Learning course! After completing this course you will be able to: · Confidently build predictive Machine Learning and Deep Learning models using R, Python to solve business problems and create business strategy · Answer Machine Learning, Deep Learning, R, Python related interview questions · Participate and perform in online Data Analytics and Data Science competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning and deep learning concepts in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning and deep learning. You will also get exposure to data science and data analysis tools like R and Python. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. It also focuses Machine Learning and Deep Learning techniques in R and Python. Most courses only focus on teaching how to run the data analysis but we believe that what happens before and after running data analysis is even more important i.e. before running data analysis it is very important that you have the right data and do some pre-processing on it. And after running data analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. Here comes the importance of machine learning and deep learning. Knowledge on data analysis tools like R, Python play an important role in these fields of Machine Learning and Deep Learning. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course. We have an in-depth knowledge on Machine Learning and Deep Learning techniques using data science and data analysis tools R, Python. We are also the creators of some of the most popular online courses - with over 600,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. We aim at providing best quality training on data science, machine learning, deep learning using R and Python through this machine learning course. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on data science, machine learning, deep learning using R and Python. Each section contains a practice assignment for you to practically implement your learning on data science, machine learning, deep learning using R and Python. Table of Contents Section 1 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Python basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning. Section 2 - R basic This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Similar to Python basics, R basics will lay foundation for gaining further knowledge on data science, machine learning and deep learning. Section 3 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation. This part of the course is instrumental in gaining knowledge data science, machine learning and deep learning in the later part of the course. Section 4 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 5 - Data Preprocessing In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 6 - Regression Model This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. Section 7 - Classification Models This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. Section 8 - Decision trees In this section, we will start with the basic theory of decision tree then we will create and plot a simple Regression decision tree. Then we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python and R Section 9 - Ensemble technique In this section, we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. We will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost. Section 10 - Support Vector Machines SVM's are unique models and stand out in terms of their concept. In this section, we will discussion about support vector classifiers and support vector machines. Section 11 - ANN Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Section 12 - Creating ANN model in Python and R In this part you will learn how to create ANN models in Python and R. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Section 13 - CNN Theoretical Concepts In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model. Section 14 - Creating CNN model in Python and R In this part you will learn how to create CNN models in Python and R. We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part. Section 15 - End-to-End Image Recognition project in Python and R In this section we build a complete image recognition project on colored images. We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition). Section 16 - Pre-processing Time Series Data In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models Section 17 - Time Series Forecasting In this section, you will learn common time series models such as Auto-regression (AR), Moving Average (MA), ARMA, ARIMA, SARIMA and SARIMAX. By the end of this course, your confidence in creating a Machine Learning or Deep Learning model in Python and R will soar. You'll have a thorough understanding of how to use ML/ DL models to create predictive models and solve real world business problems. Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Why use Python for Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. Why use R for Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/data_science_a_to_z/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Machine Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Deep Learning: Advanced Natural Language Processing and RNNs | Natural Language Processing (NLP) with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks! | 4.6 | 4926 | 26855 | Created by Lazy Programmer Inc. | Nov-22 | English | $14.99 | 8h 17m total length | https://www.udemy.com/course/deep-learning-advanced-nlp/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe. This course takes you to a higher systems level of thinking. Since you know how these things work, it’s time to build systems using these components. At the end of this course, you'll be able to build applications for problems like: text classification (examples are sentiment analysis and spam detection) neural machine translation question answering We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering. To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as: bidirectional RNNs seq2seq (sequence-to-sequence) attention memory networks All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Decent Python coding skills Understand RNNs, CNNs, and word embeddings Know how to build, train, and evaluate a neural network in Keras WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-learning-advanced-nlp/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=25K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
SQL & Database Design A-Z™: Learn MS SQL Server + PostgreSQL | Learn Both SQL Server & PostgreSQL By Doing. Enhance Your Data Analytics Career With Real World Data Science Exercises | Bestseller | 4.5 | 4899 | 28444 | Created by Kirill Eremenko, Ilya Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 12h 35m total length | https://www.udemy.com/course/sqldatabases/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | Are you interested in a career in Data Science or Data Analytics? In that case, inevitably you are going to encounter databases in your work. But how do you interact with databases? The answer is simple: SQL SQL stands for Structured Query Language and this is one of the main tools used to organize databases, input data into them and extract it on request. In this course you will learn how to create queries in a popular variation of SQL called PostgreSQL. And even if at your workplace you are using a different variation (e.g. Oracle, SQL Server or MySQL), you will find that the skills you learn in this course are easily transferable. But there are many SQL courses out there, so the question is: What makes this course stand out? The unique advantage of this course is that in addition to learning SQL you will also master the concepts of Database Design. We will cover off topics such as: - OLAP vs OLTP databases (Online Analytics Processing & Online Transaction Processing): you will understand exactly how and why the designs of these two types of Databases differ - Normalization of Databases: we will show you the theory behind normalization AND together we will practice how to normalize a Database step-by-step Why is that important? Knowing how databases are designed is not a compulsory skill to have for a Data Scientist / Analyst. However, it's a HUGE added benefit. These skills will allow you to better interact with databases and derive results and extract insights from your data faster. This course is designed with the Data Scientists and Analysts in mind, so if you want to propel your Data Science career, then this course is for you! We look forward to seeing you inside, Kirill & Ilya | https://www.udemy.com/course/sqldatabases/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | SQL | Data Scientist | >=4 | Below 10K | >=25K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Deep Learning: Convolutional Neural Networks in Python | Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning | Bestseller | 4.6 | 4864 | 32943 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 12h 16m total length | https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Convolutional Neural Network (CNN) has been used to obtain state-of-the-art results in computer vision tasks such as object detection, image segmentation, and generating photo-realistic images of people and things that don't exist in the real world! This course will teach you the fundamentals of convolution and why it's useful for deep learning and even NLP (natural language processing). You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself. This course will teach you: The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to model image data in code How to model text data for NLP (including preprocessing steps for text) How to build an CNN using Tensorflow 2 How to use batch normalization and dropout regularization in Tensorflow 2 How to do image classification in Tensorflow 2 How to do data preprocessing for your own custom image dataset How to use Embeddings in Tensorflow 2 for NLP How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition) All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. Suggested Prerequisites: matrix addition and multiplication basic probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-learning-convolutional-neural-networks-theano-tensorflow/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | ||||||||||||||||
Statistics for Data Analysis Using Excel 2016 | Plain & Simple Lessons on Descriptive & Inferential Statistics Theory With Excel Examples for Business & Six Sigma | 4.5 | 4832 | 20671 | Created by Sandeep Kumar, Quality Gurus Inc. | Jul-22 | English | $11.99 | 14h 25m total length | https://www.udemy.com/course/statistics-using-excel/ | Sandeep Kumar, Quality Gurus Inc. | Experienced Quality Director • Six Sigma Coach • Consultant | 4.5 | 49014 | 221169 | >>> Start loving data and making sense of it. Leverage the power of MS Excel to make it easy! Learn statistics, and apply these concepts in your workplace using Microsoft Excel. This course is about Statistics and Data Analysis. The course will teach you the basic concepts related to Statistics and Data Analysis and help you apply these concepts. Various examples and data sets are used to explain the application. I will explain the basic theory first, and then I will show you how to use Microsoft Excel to perform these calculations. The following areas of statistics are covered: Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot Probability - Basic Concepts, Permutations, Combinations Population and Sampling Probability Distributions - Normal, Binomial and Poisson Distributions Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, p Test, F Test, Chi-Square Test ANOVA - Perform Analysis of Variance (ANOVA) step by step by doing manual calculations and by MS Excel. What are other students saying about this course? Very structured and clear lessons on how each excel function can be used for different statistical analysis. I have benefited tremendously from attending this course. (5 stars by Kang Han Qi) I have learned a lot already and it is still a long way to go! (5 stars by Dennis Dominic Mendoza) Great course, great synergy between theory and practical used. (5 stars by Patrick Silangit) .... this is awesome and really easy to understand the statistical concepts without any stress for a guy like me who used to fail in math classes. (5 stars by Lester Deepak Martis) He's better than all my university lecturers, very clear, concise and seamless progression of the evolution of concepts in a systematic way that makes it easy to understand...Bravo!! (5 stars by Ayanda Peter) A well-planned curriculum, fantastic resources for downloads, professionally presented slides, very easy to understand, truly the best course for every financial analyst. (5 stars by Arumugam K Chandrasekar) I am fan of his teachings. it is recommended to every other person who is not confident in Statistics and want to be a pro. (5 stars by Apnatav Bhatia) Its more than my expectations, absolutely wonderful since it start with basics. (5 stars by Boikaego Raditlatla) Great course for learning business statistics or statistics in general. (5 stars by Sudesh Pandey) Brilliant course. Takes the difficult, sometimes even boring Statistics model and breaks them into easy bite size portions. Explains the theory behind it and then the Excel way of doing it. (5 stars by Karthikeyan Stalin) The self study step by step and the excel examples are very great. I can follow the course and practice on my computer alongside. Thank you for putting this whole thing together. Not a very exciting subject to teach so I appreciate being able to put this long course together to make it easy for people like us to utilize and study. (5 stars by Dr Stanley Adjabeng) What are you waiting for? This course comes with Udemy's 30 days money-back guarantee. If you are not satisfied with the course, get your money back. I hope to see you on the course. | https://www.udemy.com/course/statistics-using-excel/#instructor-1 | ASQ ConnEx Expert, PMI-PMP, IRCA Registered Lead Auditor, ASQ - CSSBB, CQA, CQE, CMQ/OE, IIA - CIA, NAHQ - CPHQ Sandeep Kumar has more than 35 years of Quality Management experience. He has worked as Quality Director on several projects, including Power, Oil and Gas and Infrastructure projects. In addition, he provides coaching and consulting services to implement Lean Six Sigma to improve performance. After the successful completion of ASQ vetting, Sandeep Kumar has been designated as a genuine and authorized ASQConnEx expert. ASQConnEx is an education delivery system and network that vets, designates, and connects quality subject matter experts with organizations to advance their excellence journey. His areas of specialization include Quality Assurance, ISO 9001:2015, Lean, Six Sigma, Risk Management, QMS Audits, Supplier Quality Surveillance, Supplier Pre-qualification, Construction Quality, Mechanical Inspection and Quality Training. Professional Qualifications: His professional qualifications/certifications include: • Authorized ASQ ConnEx Expert • ASQ-CSSBB, Certified Six Sigma Black Belt • ASQ-CMQ/OE Certified Manager of Quality/Organizational Excellence • PMI-PMP Certified Project Management Professional • IRCA Registered Lead Auditor (QMS-2015) • IIA-CIA Certified Internal Auditor • NAHQ-CPHQ Certified Professional in Healthcare Quality • ASQ-CSSGB, Certified Six Sigma Green Belt • ASQ-CQA Certified Quality Auditor • ASQ-CQE Certified Quality Engineer • ASQ-CSQP Certified Supplier Quality Professional | Statistics | Head/Director | Yes | >=4 | Below 10K | >=20K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||
Complete 2022 Data Science & Machine Learning Bootcamp | Learn Python, Tensorflow, Deep Learning, Regression, Classification, Neural Networks, Artificial Intelligence & more! | 4.6 | 4830 | 40417 | Created by Philipp Muellauer, Dr. Angela Yu | Aug-20 | English | $9.99 | 41h 16m total length | https://www.udemy.com/course/python-data-science-machine-learning-bootcamp/ | Philipp Muellauer | Data Scientist | Android Developer | Teacher | 4.6 | 4830 | 74952 | Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science. At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here's why: The course is taught by the lead instructor at the App Brewery, London's leading in-person programming bootcamp. In the course, you'll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix. This course doesn't cut any corners, there are beautiful animated explanation videos and real-world projects to build. The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback. To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup. You'll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp. We'll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional. The course includes over 40+ hours of HD video tutorials and builds your programming knowledge while solving real-world problems. In the curriculum, we cover a large number of important data science and machine learning topics, such as: Data Cleaning and Pre-Processing Data Exploration and Visualisation Linear Regression Multivariable Regression Optimisation Algorithms and Gradient Descent Naive Bayes Classification Descriptive Statistics and Probability Theory Neural Networks and Deep Learning Model Evaluation and Analysis Serving a Tensorflow Model Throughout the course, we cover all the tools used by data scientists and machine learning experts, including: Python 3 Tensorflow Pandas Numpy Scikit Learn Keras Matplotlib Seaborn SciPy SymPy By the end of this course, you will be fluently programming in Python and be ready to tackle any data science project. We’ll be covering all of these Python programming concepts: Data Types and Variables String Manipulation Functions Objects Lists, Tuples and Dictionaries Loops and Iterators Conditionals and Control Flow Generator Functions Context Managers and Name Scoping Error Handling By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer. Sign up today, and look forward to: 178+ HD Video Lectures 30+ Code Challenges and Exercises Fully Fledged Data Science and Machine Learning Projects Programming Resources and Cheatsheets Our best selling 12 Rules to Learn to Code eBook $12,000+ data science & machine learning bootcamp course materials and curriculum Don't just take my word for it, check out what existing students have to say about my courses: “One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I'm only half way through but I feel like it is some of the best money I've ever spent.” -Robert Vance “I've spent £27,000 on University..... Save some money and buy any course available by Philipp! Great stuff guys.” -Terry Woodward "This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it's not boring to follow throughout the whole course. Keep up the good work guys!" - Marvin Septianus “Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow” -Lenox James “Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.” -Andres Ariza “I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program” -Isaac Barnor “I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.” -Dale Barnes “This course has been amazing. Thanks for all the info. I'll definitely try to put this in use. :)” -Devanshika Ghosh “Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial” -Bimal Becks “English is not my native language but in this video, Phillip has great pronunciation so I don't have problem even without subtitles :)” -Dreamerx85 “Clear, precise and easy to follow instructions & explanations!” -Andreea Andrei “An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.” -Ian REMEMBER… I'm so confident that you'll love this course that we're offering a FULL money back guarantee for 30 days! So it's a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain. So what are you waiting for? Click the buy now button and join the world's best data science and machine learning course. | https://www.udemy.com/course/python-data-science-machine-learning-bootcamp/#instructor-1 | I’m Philipp, I’m a data scientist and mobile developer with a passion for teaching. I’m the lead instructor at the London App Brewery for machine learning and Android development, fluent in Python, Java, Swift, Dart, and VBA. I’ve taught thousands of students in-person in our London classroom and lead our corporate training, used by companies such as Google, Amazon and Twitter. I'm always thinking about how to make difficult concepts easy to understand, what kind of projects would make a fun tutorial, and how I can help you succeed through my courses. | Machine Learning | Data Scientist | >=4 | Below 10K | >=40K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Advanced AI: Deep Reinforcement Learning in Python | The Complete Guide to Mastering Artificial Intelligence using Deep Learning and Neural Networks | 4.5 | 4642 | 35767 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 10h 36m total length | https://www.udemy.com/course/deep-reinforcement-learning-in-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | This course is all about the application of deep learning and neural networks to reinforcement learning. If you’ve taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise. We’ve seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning. Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward. Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus - they want to reach a goal. This is such a fascinating perspective, it can even make supervised / unsupervised machine learning and "data science" seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world? While deep reinforcement learning and AI has a lot of potential, it also carries with it huge risk. Bill Gates and Elon Musk have made public statements about some of the risks that AI poses to economic stability and even our existence. As we learned in my first reinforcement learning course, one of the main principles of training reinforcement learning agents is that there are unintended consequences when training an AI. AIs don’t think like humans, and so they come up with novel and non-intuitive solutions to reach their goals, often in ways that surprise domain experts - humans who are the best at what they do. OpenAI is a non-profit founded by Elon Musk, Sam Altman (Y Combinator), and others, in order to ensure that AI progresses in a way that is beneficial, rather than harmful. Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk. One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we’ll be making heavy use of in this course. It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments. In this course, we’ll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym: CartPole Mountain Car Atari games To train effective learning agents, we’ll need new techniques. We’ll extend our knowledge of temporal difference learning by looking at the TD Lambda algorithm, we’ll look at a special type of neural network called the RBF network, we’ll look at the policy gradient method, and we’ll end the course by looking at Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic). Thanks for reading, and I’ll see you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: College-level math is helpful (calculus, probability) Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent Know how to build ANNs and CNNs in Theano or TensorFlow Markov Decision Proccesses (MDPs) Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPs WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-reinforcement-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Python | Engineer/Developer | >=4 | Below 10K | >=35K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Cluster Analysis and Unsupervised Machine Learning in Python | Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE. | Bestseller | 4.7 | 4583 | 24101 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 7h 57m total length | https://www.udemy.com/course/cluster-analysis-unsupervised-machine-learning-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Cluster analysis is a staple of unsupervised machine learning and data science. It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning. In a real-world environment, you can imagine that a robot or an artificial intelligence won’t always have access to the optimal answer, or maybe there isn’t an optimal correct answer. You’d want that robot to be able to explore the world on its own, and learn things just by looking for patterns. Do you ever wonder how we get the data that we use in our supervised machine learning algorithms? We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys. If you haven’t been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data! Those “Y”s have to come from somewhere, and a lot of the time that involves manual labor. Sometimes, you don’t have access to this kind of information or it is infeasible or costly to acquire. But you still want to have some idea of the structure of the data. If you're doing data analytics automating pattern recognition in your data would be invaluable. This is where unsupervised machine learning comes into play. In this course we are first going to talk about clustering. This is where instead of training on labels, we try to create our own labels! We’ll do this by grouping together data that looks alike. There are 2 methods of clustering we’ll talk about: k-means clustering and hierarchical clustering. Next, because in machine learning we like to talk about probability distributions, we’ll go into Gaussian mixture models and kernel density estimation, where we talk about how to "learn" the probability distribution of a set of data. One interesting fact is that under certain conditions, Gaussian mixture models and k-means clustering are exactly the same! We’ll prove how this is the case. All the algorithms we’ll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you. All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: matrix addition, multiplication probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/cluster-analysis-unsupervised-machine-learning-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=20K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Apache Spark 3 - Spark Programming in Python for Beginners | Data Engineering using Spark Structured API | 4.6 | 4498 | 25181 | Created by Prashant Kumar Pandey, Learning Journal | Jul-21 | English | $9.99 | 6h 36m total length | https://www.udemy.com/course/apache-spark-programming-in-python-for-beginners/ | Prashant Kumar Pandey | Architect, Author, Consultant, Trainer @ Learning Journal | 4.6 | 14112 | 79992 | This course does not require any prior knowledge of Apache Spark or Hadoop. We have taken enough care to explain Spark Architecture and fundamental concepts to help you come up to speed and grasp the content of this course. About the Course I am creating Apache Spark 3 - Spark Programming in Python for Beginners course to help you understand the Spark programming and apply that knowledge to build data engineering solutions. This course is example-driven and follows a working session like approach. We will be taking a live coding approach and explain all the needed concepts along the way. Who should take this Course? I designed this course for software engineers willing to develop a Data Engineering pipeline and application using the Apache Spark. I am also creating this course for data architects and data engineers who are responsible for designing and building the organization’s data-centric infrastructure. Another group of people is the managers and architects who do not directly work with Spark implementation. Still, they work with the people who implement Apache Spark at the ground level. Spark Version used in the Course This Course is using the Apache Spark 3.x. I have tested all the source code and examples used in this Course on Apache Spark 3.0.0 open-source distribution. | https://www.udemy.com/course/apache-spark-programming-in-python-for-beginners/#instructor-1 | Prashant Kumar Pandey is passionate about helping people to learn and grow in their career by bridging the gap between their existing and required skills. In his quest to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. With over 18 years of experience in IT as a developer, architect, consultant, trainer, and mentor, he has worked with international software services organizations on various data-centric and Bigdata projects. Prashant is a firm believer in lifelong continuous learning and skill development. To popularize the importance of lifelong continuous learning, he started publishing free training videos on his YouTube channel and conceptualized the idea of creating a Journal of his learning under the banner of Learning Journal. He is the founder, lead author, and chief editor of the Learning Journal portal that offers various skill development courses, training, and technical articles since the beginning of the year 2018. | Spark | Consultant | >=4 | Below 10K | >=25K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
R Programming for Statistics and Data Science 2022 | R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R | 4.6 | 4298 | 24790 | Created by 365 Careers, 365 Simona (The 365 Team) | Jan-21 | English | $9.99 | 6h 41m total length | https://www.udemy.com/course/r-programming-for-statistics-and-data-science/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | R Programming for Statistics and Data Science 2022 R Programming is a skill you need if you want to work as a data analyst or a data scientist in your industry of choice. And why wouldn't you? Data scientist is the hottest ranked profession in the US. But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title. This course is packing all of this, and more, in one easy-to-handle bundle, and it’s the perfect start to your journey. So, welcome to R for Statistics and Data Science! R for Statistics and Data Science is the course that will take you from a complete beginner in programming with R to a professional who can complete data manipulation on demand. It gives you the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others’. Laying strong foundations This course wastes no time and jumps right into hands-on coding in R. But don’t worry if you have never coded before, we start off light and teach you all the basics as we go along! We wanted this to be an equally satisfying experience for both complete beginners and those of you who would just like a refresher on R. What makes this course different from other courses? Well-paced learning. Receive top class training with content which we’ve built - and rigorously edited - to deliver powerful and efficient results. Even though preferred learning paces differ from student to student, we believe that being challenged just the right amount underpins the learning that sticks. Introductory guide to statistics. We will take you through descriptive statistics and the fundamentals of inferential statistics. We will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills. You’ll master confidence intervals and hypothesis testing, as well as regression and cluster analysis. The essentials of programming – R-based. Put yourself in the shoes of a programmer, rise above the average data scientist and boost the productivity of your operations. Data manipulation and analysis techniques in detail. Learn to work with vectors, matrices, data frames, and lists. Become adept in ‘the Tidyverse package’ - R’s most comprehensive collection of tools for data manipulation – enabling you to index and subset data, as well as spread(), gather(), order(), subset(), filter(), arrange(), and mutate() it. Create meaning-heavy data visualizations and plots. Practice makes perfect. Reinforce your learning through numerous practical exercises, made with love, for you, by us. What about homework, projects, & exercises? There is a ton of homework that will challenge you in all sorts of ways. You will have the chance to tackle the projects by yourself or reach out to a video tutorial if you get stuck. You: Is there something to show for the skills I will acquire? Us: Indeed, there is – a verifiable certificate. You will receive a verifiable certificate of completion with your name on it. You can download the certificate and attach it to your CV and even post it on your LinkedIn profile to show potential employers you have experience in carrying out data manipulations & analysis in R. If that sounds good to you, then welcome to the classroom 🙂 | https://www.udemy.com/course/r-programming-for-statistics-and-data-science/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Statistics | Yes | >=4 | Below 10K | >=20K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Deployment of Machine Learning Models | Learn how to integrate robust and reliable Machine Learning Pipelines in Production | Bestseller | 4.4 | 4263 | 27115 | Created by Soledad Galli, Christopher Samiullah | Nov-22 | English | $12.99 | 10h 22m total length | https://www.udemy.com/course/deployment-of-machine-learning-models/ | Soledad Galli | Lead Data Scientist | 4.5 | 10170 | 46124 | Welcome to Deployment of Machine Learning Models, the most comprehensive machine learning deployments online course available to date. This course will show you how to take your machine learning models from the research environment to a fully integrated production environment. What is model deployment? Deployment of machine learning models, or simply, putting models into production, means making your models available to other systems within the organization or the web, so that they can receive data and return their predictions. Through the deployment of machine learning models, you can begin to take full advantage of the model you built. Who is this course for? If you’ve just built your first machine learning models and would like to know how to take them to production or deploy them into an API, If you deployed a few models within your organization and would like to learn more about best practices on model deployment, If you are an avid software developer who would like to step into deployment of fully integrated machine learning pipelines, this course will show you how. What will you learn? We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to start creating a model in the research environment, and then transform the Jupyter notebooks into production code, package the code and deploy to an API, and add continuous integration and continuous delivery. We will discuss the concept of reproducibility, why it matters, and how to maximize reproducibility during deployment, through versioning, code repositories and the use of docker. And we will also discuss the tools and platforms available to deploy machine learning models. Specifically, you will learn: The steps involved in a typical machine learning pipeline How a data scientist works in the research environment How to transform the code in Jupyter notebooks into production code How to write production code, including introduction to tests, logging and OOP How to deploy the model and serve predictions from an API How to create a Python Package How to deploy into a realistic production environment How to use docker to control software and model versions How to add a CI/CD layer How to determine that the deployed model reproduces the one created in the research environment By the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization. What else should you know? This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure. But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course. Want to know more? Read on... This comprehensive course on deployment of machine learning models includes over 100 lectures spanning about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and re-use in your own projects. In addition, we have now included in each section an assignment where you get to reproduce what you learnt to deploy a new model. So what are you waiting for? Enroll today, learn how to put your models in production and begin extracting their true value. | https://www.udemy.com/course/deployment-of-machine-learning-models/#instructor-1 | Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science. As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations. Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning. Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics. Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions. Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities. Feel free to contact her on LinkedIn. ======================== Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos. Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones. A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina. Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos. Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones. Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades. No dudes en contactarla en LinkedIn. | Machine Learning | Chief/Lead Role | >=4 | Below 10K | >=25K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
Intro to Data Science: Your Step-by-Step Guide To Starting | Learn the critical elements of Data Science, from visualization to databases to Python and more, in just 6 weeks! | 4.4 | 4234 | 17075 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 5h 13m total length | https://www.udemy.com/course/intro-to-data-science/ | Kirill Eremenko | Data Scientist | 4.5 | 597585 | 2241495 | The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself. The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks. With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You’ll discover: * The structured path for rapidly acquiring Data Science expertise * How to build your ability in statistics to help interpret and analyse data more effectively * How to perform visualizations using one of the industry's most popular tools * How to apply machine learning algorithms with Python to solve real world problems * Why the cloud is important for Data Scientists and how to use it Along with much more. You'll pick up all the core concepts that veteran Data Scientists understand intimately. Use common industry-wide tools like SQL, Tableau and Python to tackle problems. And get guidance on how to launch your own Data Science projects. In fact, it might seem like too much at first. And there is a lot of content, exercises, study and challenges to get through. But with the right attitude, becoming a Data Scientist this quickly IS possible! Once you've finished Introduction to Data Science A-Z, you’ll be ready for an incredible career in a field that's expanding faster than almost anything else in the world. Complete this course, master the principles, and join the ranks of Data Scientists all around the world. | https://www.udemy.com/course/intro-to-data-science/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | Below 10K | >=15K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Deep Learning: Recurrent Neural Networks in Python | GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence | 4.7 | 4188 | 30763 | Created by Lazy Programmer Inc. | Nov-22 | English | $11.99 | 12h 13m total length | https://www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | *** NOW IN TENSORFLOW 2 and PYTHON 3 *** Learn about one of the most powerful Deep Learning architectures yet! The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling. This includes time series analysis, forecasting and natural language processing (NLP). Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models. This course will teach you: The basics of machine learning and neurons (just a review to get you warmed up!) Neural networks for classification and regression (just a review to get you warmed up!) How to model sequence data How to model time series data How to model text data for NLP (including preprocessing steps for text) How to build an RNN using Tensorflow 2 How to use a GRU and LSTM in Tensorflow 2 How to do time series forecasting with Tensorflow 2 How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!) How to use Embeddings in Tensorflow 2 for NLP How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition) All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: matrix addition, multiplication basic probability (conditional and joint distributions) Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Python & Machine Learning for Financial Analysis | Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance | 4.5 | 4007 | 96443 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard | Jul-22 | English | $11.99 | 23h 1m total length | https://www.udemy.com/course/ml-and-python-in-finance-real-cases-and-practical-solutions/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking? If the answer is yes, then welcome to the “The Complete Python and Machine Learning for Financial Analysis” course in which you will learn everything you need to develop practical real-world finance/banking applications in Python! So why Python? Python is ranked as the number one programming language to learn in 2020, here are 6 reasons you need to learn Python right now! 1. #1 language for AI & Machine Learning: Python is the #1 programming language for machine learning and artificial intelligence. 2. Easy to learn: Python is one of the easiest programming language to learn especially of you have not done any coding in the past. 3. Jobs: high demand and low supply of python developers make it the ideal programming language to learn now. 4. High salary: Average salary of Python programmers in the US is around $116 thousand dollars a year. 5. Scalability: Python is extremely powerful and scalable and therefore real-world apps such as Google, Instagram, YouTube, and Spotify are all built on Python. 6. Versatility: Python is the most versatile programming language in the world, you can use it for data science, financial analysis, machine learning, computer vision, data analysis and visualization, web development, gaming and robotics applications. This course is unique in many ways: 1. The course is divided into 3 main parts covering python programming fundamentals, financial analysis in Python and AI/ML application in Finance/Banking Industry. A detailed overview is shown below: a) Part #1 – Python Programming Fundamentals: Beginner’s Python programming fundamentals covering concepts such as: data types, variables assignments, loops, conditional statements, functions, and Files operations. In addition, this section will cover key Python libraries for data science such as Numpy and Pandas. Furthermore, this section covers data visualization tools such as Matplotlib, Seaborn, Plotly, and Bokeh. b) Part #2 – Financial Analysis in Python: This part covers Python for financial analysis. We will cover key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio. In addition, we will cover Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier. We will also cover trading strategies such as momentum-based and moving average trading. c) Part #3 – AI/Ml in Finance/Banking: This section covers practical projects on AI/ML applications in Finance. We will cover application of Deep Neural Networks such as Long Short Term Memory (LSTM) networks to perform stock price predictions. In addition, we will cover unsupervised machine learning strategies such as K-Means Clustering and Principal Components Analysis to perform Baking Customer Segmentation or Clustering. Furthermore, we will cover the basics of Natural Language Processing (NLP) and apply it to perform stocks sentiment analysis. 2. There are several mini challenges and exercises throughout the course and you will learn by doing. The course contains mini challenges and coding exercises in almost every video so you will learn in a practical and easy way. 3. The Project-based learning approach: you will build more than 6 full practical projects that you can add to your portfolio of projects to showcase your future employer during job interviews. So who is this course for? This course is geared towards the following: Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs. Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors. Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience. There is no prior experience required, Even if you have never used python or any programming language before, don’t worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge. In this course, (1) you will have a true practical project-based learning experience, we will build more than 6 projects together (2) You will have access to all the codes and slides, (3) You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in python programming to employers. (4) All of this comes with a 30 day money back guarantee so you can give a course a try risk free! Check out the preview videos and the outline to get an idea of the projects we will be covering. Enroll today and I look forward to seeing you inside! | https://www.udemy.com/course/ml-and-python-in-finance-real-cases-and-practical-solutions/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=50K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Deep Learning Prerequisites: Logistic Regression in Python | Data science, machine learning, and artificial intelligence in Python for students and professionals | Bestseller | 4.7 | 3792 | 27673 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 6h 19m total length | https://www.udemy.com/course/data-science-logistic-regression-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free. This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want use your skills to make data-driven decisions and optimize your business using scientific principles, then this course is for you. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/data-science-logistic-regression-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=25K | >=4 | >=1 Lakh | >=5 Lakh | ||||||||||||||||
Mathematical Foundations of Machine Learning | Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch | Bestseller | 4.6 | 3742 | 100066 | Created by Dr Jon Krohn, Ligency I Team, Ligency Team | Jul-22 | English | $11.99 | 16h 25m total length | https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/ | Dr Jon Krohn | Chief Data Scientist and #1 Bestselling Author | 4.6 | 3765 | 100320 | Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career. Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely linear algebra and calculus — that underlies machine learning algorithms and data science models. Course Sections Linear Algebra Data Structures Tensor Operations Matrix Properties Eigenvectors and Eigenvalues Matrix Operations for Machine Learning Limits Derivatives and Differentiation Automatic Differentiation Partial-Derivative Calculus Integral Calculus Throughout each of the sections, you'll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form! This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding bonus content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total. Are you ready to become an outstanding data scientist? See you in the classroom. | https://www.udemy.com/course/machine-learning-data-science-foundations-masterclass/#instructor-1 | Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University and New York University, as well as online via O'Reilly and the SuperDataScience podcast. He holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times. | Machine Learning | Chief/Lead Role | >=4 | Below 10K | >=1 Lakh | >=4 | Below 10 K | >=1 Lakh | ||||||||||||||||
The Complete Self-Driving Car Course - Applied Deep Learning | Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python | Bestseller | 4.6 | 3714 | 21420 | Created by Rayan Slim, Amer Sharaf, Jad Slim, Sarmad Tanveer | May-21 | English | $13.99 | 18h 7m total length | https://www.udemy.com/course/applied-deep-learningtm-the-complete-self-driving-car-course/ | Rayan Slim | Developer | 4.6 | 22052 | 165102 | Self-driving cars have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. Learn & Master Deep Learning in this fun and exciting course with top instructor Rayan Slim. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course. You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen. By the end of the course, you will have built a fully functional self-driving car fuelled entirely by Deep Learning. This powerful simulation will impress even the most senior developers and ensure you have hands on skills in neural networks that you can bring to any project or company. This course will show you how to: Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car. Learn to train a Perceptron-based Neural Network to classify between binary classes. Learn to train Convolutional Neural Networks to identify between various traffic signs. Train Deep Neural Networks to fit complex datasets. Master Keras, a power Neural Network library written in Python. Build and train a fully functional self driving car to drive on its own! No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers. This course also comes with all the source code and friendly support in the Q&A area. | https://www.udemy.com/course/applied-deep-learningtm-the-complete-self-driving-car-course/#instructor-1 | Rayan is a full-stack software developer based in Ottawa, Canada. Rayan has been appointed as an acting tech lead at Canada's IRCC. His main role is to set up infrastructure monitoring tools to extract health metrics from cloud-native applications. Rayan also takes leadership roles as he guides other developers towards building Spring Boot applications that implement Enterprise Integration Patterns using the Apache Camel framework. His supervision extends to showing developers how to deploy their applications on the Red Hat Openshift platform using the Kubernetes package manager Helm. Outside of his daily work, Rayan loves to explore new technologies. He is deeply passionate about Artificial Intelligence and Data Visualization. In Rayan's free time, he loves to teach! | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=20K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
PyTorch for Deep Learning with Python Bootcamp | Learn how to create state of the art neural networks for deep learning with Facebook's PyTorch Deep Learning library! | 4.6 | 3612 | 23736 | Created by Jose Portilla | Sep-19 | English | $13.99 | 17h 0m total length | https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to the best online course for learning about Deep Learning with Python and PyTorch! PyTorch is an open source deep learning platform that provides a seamless path from research prototyping to production deployment. It is rapidly becoming one of the most popular deep learning frameworks for Python. Deep integration into Python allows popular libraries and packages to be used for easily writing neural network layers in Python. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. This course focuses on balancing important theory concepts with practical hands-on exercises and projects that let you learn how to apply the concepts in the course to your own data sets! When you enroll in this course you will get access to carefully laid out notebooks that explain concepts in an easy to understand manner, including both code and explanations side by side. You will also get access to our slides that explain theory through easy to understand visualizations. In this course we will teach you everything you need to know to get started with Deep Learning with Pytorch, including: NumPy Pandas Machine Learning Theory Test/Train/Validation Data Splits Model Evaluation - Regression and Classification Tasks Unsupervised Learning Tasks Tensors with PyTorch Neural Network Theory Perceptrons Networks Activation Functions Cost/Loss Functions Backpropagation Gradients Artificial Neural Networks Convolutional Neural Networks Recurrent Neural Networks and much more! By the end of this course you will be able to create a wide variety of deep learning models to solve your own problems with your own data sets. So what are you waiting for? Enroll today and experience the true capabilities of Deep Learning with PyTorch! I'll see you inside the course! -Jose | https://www.udemy.com/course/pytorch-for-deep-learning-with-python-bootcamp/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | PyTorch | Head/Director | >=4 | Below 10K | >=20K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Introduction to Natural Language Processing (NLP) | Learn how to analyze text data. | 4.5 | 3565 | 13707 | Created by Brian Sacash | Mar-18 | English | $9.99 | 3h 0m total length | https://www.udemy.com/course/natural-language-processing/ | Brian Sacash | Data Scientist | 4.5 | 3565 | 13706 | This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you'll get hands on experience working with and analyzing text. As a student of this course, you'll get updates for free, which include lecture revisions, new code examples, and new data projects. By the end of this course you will: Have an understanding of how to use the Natural Language Tool Kit. Be able to load and manipulate your own text data. Know how to formulate solutions to text based problems. Know when it is appropriate to apply solutions such as sentiment analysis and classification techniques. | https://www.udemy.com/course/natural-language-processing/#instructor-1 | Brian has a BS in physics and MS in Quantitative Analysis from the University of Cincinnati with over 10 years experience in data analysis. Over the course of his career he has developed a skill set in natural language processing analysis and big data. He has helped solved data problems from the Department of Defense to global financial institutions. He previously was a consultant where he helped organizations understand their data through advanced analytics methods. Brian currently works on developing methods and software for large data systems. | NLP | Data Scientist | >=4 | Below 10K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Unsupervised Machine Learning Hidden Markov Models in Python | HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank. | Bestseller | 4.6 | 3552 | 25017 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 9h 45m total length | https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you’re going to default. In short, sequences are everywhere, and being able to analyze them is an important skill in your data science toolbox. The easiest way to appreciate the kind of information you get from a sequence is to consider what you are reading right now. If I had written the previous sentence backwards, it wouldn’t make much sense to you, even though it contained all the same words. So order is important. While the current fad in deep learning is to use recurrent neural networks to model sequences, I want to first introduce you guys to a machine learning algorithm that has been around for several decades now - the Hidden Markov Model. This course follows directly from my first course in Unsupervised Machine Learning for Cluster Analysis, where you learned how to measure the probability distribution of a random variable. In this course, you’ll learn to measure the probability distribution of a sequence of random variables. You guys know how much I love deep learning, so there is a little twist in this course. We’ve already covered gradient descent and you know how central it is for solving deep learning problems. I claimed that gradient descent could be used to optimize any objective function. In this course I will show you how you can use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm. We’re going to do it in Theano and Tensorflow, which are popular libraries for deep learning. This is also going to teach you how to work with sequences in Theano and Tensorflow, which will be very useful when we cover recurrent neural networks and LSTMs. This course is also going to go through the many practical applications of Markov models and hidden Markov models. We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you. HMMs have been very successful in natural language processing or NLP. We’ll look at what is possibly the most recent and prolific application of Markov models - Google’s PageRank algorithm. And finally we’ll discuss even more practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology - how is DNA, the code of life, translated into physical or behavioral attributes of an organism? All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Numpy and Matplotlib, along with a little bit of Theano. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus linear algebra probability Be comfortable with the multivariate Gaussian distribution Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/unsupervised-machine-learning-hidden-markov-models-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=25K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Streaming Big Data with Spark Streaming and Scala | Spark Streaming tutorial covering Spark Structured Streaming, Kafka integration, and streaming big data in real-time. | 4 | 3352 | 25331 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Oct-22 | English | $10.99 | 6h 26m total length | https://www.udemy.com/course/taming-big-data-with-spark-streaming-hands-on/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 137161 | 661038 | WARNING: This course includes activities that involve Twitter integration, using an API Twitter has recently disabled. Following along hands-on is no longer possible for these activities, but you can still learn about streaming from watching the videos. "Big Data" analysis is a hot and highly valuable skill. Thing is, "big data" never stops flowing! Spark Streaming is a new and quickly developing technology for processing massive data sets as they are created - why wait for some nightly analysis to run when you can constantly update your analysis in real time, all the time? Whether it's clickstream data from a big website, sensor data from a massive "Internet of Things" deployment, financial data, or something else - Spark Streaming is a powerful technology for transforming and analyzing that data right when it is created, all the time. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb. This course gets your hands on to some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models! You'll write and run real Spark Streaming jobs right at home on your own PC, and toward the end of the course, we'll show you how to take those jobs to a real Hadoop cluster and run them in a production environment too. Across over 30 lectures and almost 6 hours of video content, you'll: Get a crash course in the Scala programming language Learn how Apache Spark operates on a cluster Set up discretized streams with Spark Streaming and transform them as data is received Use structured streaming to stream into dataframes in real-time Analyze streaming data over sliding windows of time Maintain stateful information across streams of data Connect Spark Streaming with highly scalable sources of data, including Kafka, Flume, and Kinesis Dump streams of data in real-time to NoSQL databases such as Cassandra Run SQL queries on streamed data in real time Train machine learning models in real time with streaming data, and use them to make predictions that keep getting better over time Package, deploy, and run self-contained Spark Streaming code to a real Hadoop cluster using Amazon Elastic MapReduce. This course is filled with achievable activities and exercises to reinforce your learning. By the end of this course, you'll be confidently creating Spark Streaming scripts in Scala, and be prepared to tackle massive streams of data in a whole new way. You'll be surprised at how easy Spark Streaming makes it! | https://www.udemy.com/course/taming-big-data-with-spark-streaming-hands-on/#instructor-1 | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Big Data/Data Engineer | Founder/Entrepreneur | >=4 | Below 10K | >=25K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Artificial Intelligence for Business | Solve Real World Business Problems with AI Solutions | 4.4 | 3279 | 23635 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $13.99 | 15h 4m total length | https://www.udemy.com/course/ai-for-business/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1623570 | Structure of the course: Part 1 - Optimizing Business Processes Case Study: Optimizing the Flows in an E-Commerce Warehouse AI Solution: Q-Learning Part 2 - Minimizing Costs Case Study: Minimizing the Costs in Energy Consumption of a Data Center AI Solution: Deep Q-Learning Part 3 - Maximizing Revenues Case Study: Maximizing Revenue of an Online Retail Business AI Solution: Thompson Sampling Real World Business Applications: With Artificial Intelligence, you can do three main things for any business: Optimize Business Processes Minimize Costs Maximize Revenues We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind. In Part 3 - Maximizing Revenues, we will build a different AI that will maximize revenue of an Online Retail Business, making it earn more than 1 Billion dollars in revenue! But that's not all, this time, and for the first time, we’ve prepared a huge innovation for you. With this course, you will get an incredible extra product, highly valuable for your career: "a 100-pages book covering everything about Artificial Intelligence for Business!". The Book: This book includes: 100 pages of crystal clear explanations, written in beautiful and clean latex All the AI intuition and theory, including the math explained in detail The three Case Studies of the course, and their solutions Three different AI models, including Q-Learning, Deep Q-Learning, and Thompson Sampling Code Templates Homework and their solutions for you to practice Plus, lots of extra techniques and tips like saving and loading models, early stopping, and much much more. Conclusion: If you want to land a top-paying job or create your very own successful business in AI, then this is the course you need. Take your AI career to new heights today with Artificial Intelligence for Business -- the ultimate AI course to propel your career further. | https://www.udemy.com/course/ai-for-business/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Artificial Intelligence | Founder/Entrepreneur | >=4 | Below 10K | >=20K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
AWS Certified Machine Learning Specialty (MLS-C01) | Hands on AWS ML SageMaker Course with Practice Test. Join Live Study Group Q&A! | 4.5 | 3247 | 26945 | Created by Chandra Lingam | Oct-22 | English | $15.99 | 17h 51m total length | https://www.udemy.com/course/aws-machine-learning-a-complete-guide-with-python/ | Chandra Lingam | Compute With Cloud Inc | 4.5 | 12557 | 106364 | Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep *** NEW Labs - A/B Testing, Multi-model endpoints *** *** NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness *** *** New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime *** *** Lab notebook now use spot-training as the default option. Save over 60% in training costs *** *** NEW: Nuts and Bolts of Optimization, quizzes *** *** All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK *** *** Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest. With labs. *** *** Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs *** *** Timed Practice Test and additional lectures for Exam Preparation added Welcome to AWS Machine Learning Specialty Course! I am Chandra Lingam, and I am your instructor In this course, you will gain first-hand SageMaker experience with many hands-on labs that demonstrates specific concepts We start with how to set up your SageMaker environment If you are new to ML, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model These topics are very important for an ML practitioner as well as for the certification exam SageMaker uses containers to wrap your favorite algorithms and frameworks such as Pytorch, and TensorFlow The advantage of a container-based approach is it provides a standard interface to build and deploy your models It is also straightforward to convert your model into a production application In a series of concise labs, you will in fact train, deploy, and invoke your first SageMaker model Like any other software project, ML Solution also requires continuous improvement We look at how to safely incorporate new changes in a production system, perform A/B testing, and even rollback changes when necessary All with zero downtime to your application We then look at emerging social trends on the fairness of Machine learning and AI systems. What will you do if your users accuse your model as racially biased or gender-biased? How will you handle it? In this section, we look at the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them We then look at Cloud security – how to protect your data and model from unauthorized use You will also learn about recommender systems to incorporate features such as movie and product recommendation The algorithms that you learn in the course are state of the art, and tuning them for your dataset is especially challenging So, we look at how to tune your model with automated tools You will gain experience in time series forecasting Anomaly detection and building custom deep learning models With the knowledge, you gain here and the included high-quality practice exam, you will easily achieve the certification! And something unique that I offer my students is a weekly study group meeting to discuss and clarify any questions I am looking forward to seeing you! Thank you! | https://www.udemy.com/course/aws-machine-learning-a-complete-guide-with-python/#instructor-1 | I am truly honored Udemy CEO highlighted my SAA-C03 courses in the recent earnings call for providing up-to-date and relevant content. Chandra Lingam is an expert on Amazon Web Services, mission-critical systems, and machine learning. He has a rich background in systems development in both traditional IT data center and on the Cloud. He is uniquely positioned to guide you to become an expert in AWS Cloud Platform. Before becoming a full-time course developer and instructor, he spent 15 years at Intel as a software engineer. He has a Master's degree in Computer Science from Arizona State University, Tempe, and a Bachelor's degree in Computer Science from Thiagarajar College of Engineering, Madurai | Machine Learning | >=4 | Below 10K | >=25K | >=4 | Below 1 Lakh | >=1 Lakh | ||||||||||||||||||
Deep Learning with Python and Keras | Understand and build Deep Learning models for images, text and more using Python and Keras | Bestseller | 4.7 | 3072 | 23030 | Created by Data Weekends, Jose Portilla, Francesco Mosconi | Dec-18 | English | $14.99 | 9h 56m total length | https://www.udemy.com/course/deep-learning-with-python-and-keras/ | Data Weekends | Learn the essentials of Data Science in just one weekend | 4.5 | 3112 | 24265 | This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications. This course is a good balance between theory and practice. We don't shy away from explaining mathematical details and at the same time we provide exercises and sample code to apply what you've just learned. The goal is to provide students with a strong foundation, not just theory, not just scripting, but both. At the end of the course you'll be able to recognize which problems can be solved with Deep Learning, you'll be able to design and train a variety of Neural Network models and you'll be able to use cloud computing to speed up training and improve your model's performance. | https://www.udemy.com/course/deep-learning-with-python-and-keras/#instructor-1 | Data Weekends™ are accelerated data science workshop for programmers where you can quickly learn to apply predictive analytics to real-world data. We offer courses in Data Analytics, Machine Learning, Deep Learning and Reinforcement Learning. Through our parent company Catalit LLC we also offer corporate training and consulting on Data Science, Machine Learning and Deep Learning. Data Weekends' founder and lead instructor is Francesco Mosconi, PhD. | Deep Learning | >=4 | Below 10K | >=20K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
An Introduction to Machine Learning for Data Engineers | A Prerequisite for Tensorflow on Google's Cloud Platform for Data Engineers | 4.3 | 2988 | 9320 | Created by Mike West | Feb-22 | English | $9.99 | 1h 10m total length | https://www.udemy.com/course/an-introduction-to-machine-learning-for-data-engineers/ | Mike West | Creator of LogikBot | 4.3 | 19947 | 236918 | THE REVIEWS ARE IN: Another Excellent course from a brilliant Instructor. Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google's Cloud. -- Julie Johnson Awesome! -- Satendra Great learning experience!! -- Lakshminarayana Wonderful learning... -- Rajesh Excellent -- Dipthi Clear and to the point. Fit's a lot of knowledge into short, easy to understand concepts/thoughts/scenarios. -- Sam Course was fantastic. -- Narsh Great overview of ML -- Eli Very helpful for beginners, All concept explained well. Overall insightful training session. Thank you ! --Vikas Very good training. Concepts were well explained. -- Jose I like the real world touch given to course material . This is extremely important. -- Soham Learned some new terms and stuffs in Machine Learning. Ideal for learners who needs to get some overview of ML. -- Akilan This session is very good and giving more knowledge about machine learning -- Neethu Got to know many things on machine learning with data as a beginner. Thanks Mike. --Velumani Really well explained and very informative. -- Vinoth COURSE INTRODUCTION: Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers. This course will show you the basics of machine learning for data engineers. The course is geared towards answering questions for the Google Certified Data Engineering exam. This is NOT a general course or introduction to machine learning. This is a very focused course for learning the concepts you'll need to know to pass the Google Certified Data Engineering Exam. At this juncture, the Google Certified Data Engineer is the only real world certification for data and machine learning engineers. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed.” The vast majority of applied machine learning is supervised machine learning. The word applied means you build models in the real world. Supervised machine learning is a type of machine learning that involves building models from data that exists. A good way to think about supervised machine learning is: If you can get your data into a tabular format, like that of an excel spreadsheet, then most machine learning models can model it. In the course, we’ll learn the different types of algorithms used. We will also cover the nomenclature specific to machine learning. Every discipline has their own vernacular and data science is not different. You’ll also learn why the Python programming language has emerged as the gold standard for building real world machine learning models. Additionally, we will write a simple neural network and walk through the process and the code step by step. Understanding the code won't be as important as understanding the importance and effectiveness of one simple artificial neuron. *Five Reasons to take this Course.* 1) You Want to be a Data Engineer It's the number one job in the world. (not just within the computer space) The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. 2) The Google Certified Data Engineer Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone. Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google. 3) The Growth of Data is Insane Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 Exabytes a day. That number doubles every month. 4) Machine Learning in Plain English Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer. Google expects data engineers to be able to build machine learning models. In this course, we will cover all the basics of machine learning at a very high level. 5) You want to be ahead of the Curve The data engineer role is fairly new. While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package. Thanks for your interest in An Introduction to Machine Learning for Data Engineers. | https://www.udemy.com/course/an-introduction-to-machine-learning-for-data-engineers/#instructor-1 | I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help. I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space. Experience, education and passion I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car. Certifications Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT). Personal Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children. | Data Engineer | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||||
R Level 1 - Data Analytics with R | Use R for Data Analytics and Data Mining | 4.6 | 2973 | 15096 | Created by R-Tutorials Training | Apr-19 | English | $11.99 | 8h 42m total length | https://www.udemy.com/course/r-level1/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263421 | Are you new to R? Do you want to learn more about statistical programming? Are you in a quantitative field? You just started learning R but you struggle with all the free but unorganized material available elsewhere? Do you want to hack the learning curve and stay ahead of your competition? If your answer is YES to some of those points - read on! This Tutorial is the first step - your Level 1 - to R mastery. All the important aspects of statistical programming ranging from handling different data types to loops and functions, even graphs are covered. While planing this course I used the Pareto 80/20 principle. I filtered for the most useful items in the R language which will give you a quick and efficient learning experience. Learning R will help you conduct your projects. On the long run it is an invaluable skill which will enhance your career. Your journey will start with the theoretical background of object and data types. You will then learn how to handle the most common types of objects in R. Much emphasis is put on loops in R since this is a crucial part of statistical programming. It is also shown how the apply family of functions can be used for looping.In the graphics section you will learn how to create and tailor your graphs. As an example we will create boxplots, histograms and piecharts. Since the graphs interface is quite the same for all types of graphs, this will give you a solid foundation.With the R Commander you will also learn about an alternative to RStudio. Especially for classic hypthesis tests the R Coomander GUI can save you some time.According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. Furthermore you can also check out the r-tutorials R exercise database over at our webpage. In the database you will find more exercises on the topics of this course.You can download the code pdf of every section to try the presented code on your own. This tutorial is your first step to benefit from this open source software. What R you waiting for? Martin | https://www.udemy.com/course/r-level1/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Data Analyst | >=4 | Below 10K | >=15K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Modern Deep Learning in Python | Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS. | 4.8 | 2963 | 32138 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 11h 20m total length | https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you. You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time. You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training. Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future. In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano. With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence. Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best. Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network. With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?" These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Know about gradient descent Probability and statistics Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Know how to write a neural network with Numpy WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/data-science-deep-learning-in-theano-tensorflow/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=30K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Machine Learning with Javascript | Master Machine Learning from scratch using Javascript and TensorflowJS with hands-on projects. | 4.7 | 2841 | 27178 | Created by Stephen Grider | Nov-22 | English | $13.99 | 17h 40m total length | https://www.udemy.com/course/machine-learning-with-javascript/ | Stephen Grider | Engineering Architect | 4.6 | 394933 | 1159092 | If you're here, you already know the truth: Machine Learning is the future of everything. In the coming years, there won't be a single industry in the world untouched by Machine Learning. A transformative force, you can either choose to understand it now, or lose out on a wave of incredible change. You probably already use apps many times each day that rely upon Machine Learning techniques. So why stay in the dark any longer? There are many courses on Machine Learning already available. I built this course to be the best introduction to the topic. No subject is left untouched, and we never leave any area in the dark. If you take this course, you will be prepared to enter and understand any sub-discipline in the world of Machine Learning. A common question - Why Javascript? I thought ML was all about Python and R? The answer is simple - ML with Javascript is just plain easier to learn than with Python. Although it is immensely popular, Python is an 'expressive' language, which is a code-word that means 'a confusing language'. A single line of Python can contain a tremendous amount of functionality; this is great when you understand the language and the subject matter, but not so much when you're trying to learn a brand new topic. Besides Javascript making ML easier to understand, it also opens new horizons for apps that you can build. Rather than being limited to deploying Python code on the server for running your ML code, you can build single-page apps, or even browser extensions that run interesting algorithms, which can give you the possibility of developing a completely novel use case! Does this course focus on algorithms, or math, or Tensorflow, or what?!?! Let's be honest - the vast majority of ML courses available online dance around the confusing topics. They encourage you to use pre-build algorithms and functions that do all the heavy lifting for you. Although this can lead you to quick successes, in the end it will hamper your ability to understand ML. You can only understand how to apply ML techniques if you understand the underlying algorithms. That's the goal of this course - I want you to understand the exact math and programming techniques that are used in the most common ML algorithms. Once you have this knowledge, you can easily pick up new algorithms on the fly, and build far more interesting projects and applications than other engineers who only understand how to hand data to a magic library. Don't have a background in math? That's OK! I take special care to make sure that no lecture gets too far into 'mathy' topics without giving a proper introduction to what is going on. A short list of what you will learn: Advanced memory profiling to enhance the performance of your algorithms Build apps powered by the powerful Tensorflow JS library Develop programs that work either in the browser or with Node JS Write clean, easy to understand ML code, no one-name variables or confusing functions Pick up the basics of Linear Algebra so you can dramatically speed up your code with matrix-based operations. (Don't worry, I'll make the math easy!) Comprehend how to twist common algorithms to fit your unique use cases Plot the results of your analysis using a custom-build graphing library Learn performance-enhancing strategies that can be applied to any type of Javascript code Data loading techniques, both in the browser and Node JS environments | https://www.udemy.com/course/machine-learning-with-javascript/#instructor-1 | Stephen Grider has been building complex Javascript front ends for top corporations in the San Francisco Bay Area. With an innate ability to simplify complex topics, Stephen has been mentoring engineers beginning their careers in software development for years, and has now expanded that experience onto Udemy, authoring the highest rated React course. He teaches on Udemy to share the knowledge he has gained with other software engineers. Invest in yourself by learning from Stephen's published courses. | Machine Learning | Architect | >=4 | Below 10K | >=25K | >=4 | >=3.5 Lakh | >=10 Lakh | |||||||||||||||||
Artificial Intelligence & Machine Learning for Business | The Ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs | 4.5 | 2812 | 9771 | Created by Analytics Vidhya | Jun-19 | English | $11.99 | 5h 42m total length | https://www.udemy.com/course/artificial-intelligence-machine-learning-business/ | Analytics Vidhya | Data Science Community | 4.5 | 3229 | 28864 | Are you prepared for the inevitable AI revolution? How can you leverage it in your current role as a business leader (whether that's a manager, team leader or a CxO)? Analytics Vidhya’s ‘Artificial Intelligence (AI) & Machine Learning (ML) for Business’ course, curated and delivered by experienced instructors, will help you understand the answers to these pressing questions. Artificial Intelligence has become the centrepiece of strategic decision making for organizations. It is disrupting the way industries function - from sales and marketing to finance and HR, companies are betting on AI to give them a competitive edge. AI for Business Leaders is a thoughtfully created course designed specifically for business people and does not require any programming. Through this course you will learn about the current state of AI, how it's disrupting businesses globally and in diverse fields, how it might impact your current role and what you can do about it. This course also dives into the various building blocks of AI and why it's necessary for you to have a high-level overview of these topics in today's data-driven world. We will also provide you with multiple practical case studies towards the end of the course that will test your understanding and add context to all that you've studied. By the time you finish the course, you will be ready to apply your newly-acquired knowledge in your current organization. You will be able to make informed strategic decisions for yourself and your business. | https://www.udemy.com/course/artificial-intelligence-machine-learning-business/#instructor-1 | Analytics Vidhya is one of the largest Analytics and Data Science community across the globe. We aim to create next generation data science ecosystem by democratising Artificial Intelligence, Machine Learning and Data Science. Our courses are easy to understand, practical and inspired by real life applications of Artificial Intelligence in Businesses. | Artificial Intelligence | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
R Programming For Absolute Beginners | Learn the basics of writing code in R - your first step to become a data scientist | 4.6 | 2800 | 154125 | Created by Bogdan Anastasiei | Jun-17 | English | $9.99 | 9h 32m total length | https://www.udemy.com/course/r-programming-course-for-absolute-beginners/ | Bogdan Anastasiei | University Teacher and Consultant | 4.5 | 7750 | 296607 | If you have decided to learn R as your data science programming language, you have made an excellent decision! R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist. The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course. This course contains about 100 video lectures in nine sections. In the first section of this course you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables. The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more. These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling. After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions. The next section is about working with strings. Here we will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string and especially how to create regular expressions that identify patterns in strings. In the following section you’ll learn how to build charts in R. We are going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line and boxplot. Moreover, you will learn how to plot a function of one variable and how to export the charts you create. Every command and function is visually explained: you can see the output live. At the end of each section you will find a PDF file with practical exercises that allow you to apply and strengthen your knowledge. So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey! | https://www.udemy.com/course/r-programming-course-for-absolute-beginners/#instructor-1 | My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting. | Misc | Consultant | >=4 | Below 10K | >=1 Lakh | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Python-Introduction to Data Science and Machine learning A-Z | Python basics Learn Python for Data Science Python For Machine learning and Python Tips and tricks | 4.3 | 2756 | 248772 | Created by Yassin Marco | Apr-22 | English | $9.99 | 7h 20m total length | https://www.udemy.com/course/python-introduction-to-data-science-and-machine-learning-a-z/ | Yassin Marco | Helped over 1 300 000+ students in 198 countries | 4.2 | 45208 | 1259455 | Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. But, this course will give all the basics you need no matter for what objective you want to use it so if you : - Are a student and want to improve your programming skills and want to learn new utilities on how to use Python - Need to learn basics of Data science - Have to understand basic Data science tools to improve your career - Simply acquire the skills for personal use Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects. The structure of the course This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools. Indeed, you will at first learn all the mathematics that are associated with Data science. This means that you will have a complete introduction to the majority of important statistical formulas and functions that exist. You will also learn how to set up and use Jupyter as well as Pycharm to write your Python code. After, you are going to learn different Python libraries that exist and how to use them properly. Here you will learn tools such as NumPy or SciPy and many others. Finally, you will have an introduction to machine learning and learn how a machine learning algorithm works. All this in just one course. Another very interesting thing about this course it contains a lot of practice. Indeed, I build all my course on a concept of learning by practice. In other words, this course contains a lot of practice this way you will be able to be sure that you completely understand each concept by writing the code yourself. For who is this course designed This course is designed for beginner that are interested to have a basic understand of what exactly Data science is and be able to perform it with python programming language. Since this is an introduction to Data science, you don't have to be a specialist to understand the course. Of course having some basic prior python knowledge could be good but it's not mandatory to be able to understand this course. Also, if you are a student and wish to learn more about Data science or you simply want to improve your python programming skills by learning new tools you will definitely enjoy this course. Finally, this course is for any body that is interested to learn more about Data science and how to properly use python to be able to analyze data with different tools. Why should I take this course If you want to learn all the basics of Data science and Python this course has all you need. Not only you will have a complete introduction to Data science but you will also be able to practice python programming in the same course. Indeed, this course is created to help you learn new skills as well as improving your current programming skills. There is no risk involved in taking this course This course comes with a 100% satisfaction guarantee, this means that if your are not happy with what you have learned, you have 30 days to get a complete refund with no questions asked. Also, if there is any concept that you find complicated or you are just not able to understand, you can directly contact me and it will be my pleasure to support you in your learning. This means that you can either learn amazing skills that can be very useful in your professional or everyday life or you can simply try the course and if you don't like it for any reason ask for a refund. You can't lose with this type of offer !! ENROLL NOW and start learning today 🙂 | https://www.udemy.com/course/python-introduction-to-data-science-and-machine-learning-a-z/#instructor-1 | Yassin has a BS in international management and multiple certifications in management and IT. He works on a daily basis with various Microsoft apps and is a specialist in excel as well as in various other fields such as online business creation and promotion, marketing, and many more. Also, he has a passion for finances and has helped many people in taking their first steps in the trading and investing world, from basic financial coaching to advanced Stock/Forex data analysis. He has developed a passion for coaching and educating and has helped more than 1.1 Million students on multiple online platforms. Teaching in English and French, he has been able to reach across to people spanning from over 198 countries. Yassin is highly committed to what he does and is very easy to contact so in case you have any questions about his current or future courses, please don't hesitate to contact him here on Udemy. Enjoy your learning 🙂 | Machine Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Feature Engineering for Machine Learning | Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more. | 4.6 | 2714 | 18627 | Created by Soledad Galli | Jun-22 | English | $9.99 | 10h 28m total length | https://www.udemy.com/course/feature-engineering-for-machine-learning/ | Soledad Galli | Lead Data Scientist | 4.5 | 10170 | 46124 | Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn about variable imputation, variable encoding, feature transformation, discretization, and how to create new features from your data. Master Feature Engineering and Feature Extraction. In this course, you will learn multiple feature engineering methods that will allow you to transform your data and leave it ready to train machine learning models. Specifically, you will learn: How to impute missing data How to encode categorical variables How to transform numerical variables and change their distribution How to perform discretization How to remove outliers How to extract features from date and time How to create new features from existing ones Create useful Features with Math, Statistics and Domain Knowledge Feature engineering is the process of transforming existing features or creating new variables for use in machine learning. Raw data is not suitable to train machine learning algorithms. Instead, data scientists devote a lot of time to data preprocessing. This course teaches you everything you need to know to leave your data ready to train your models. While most online courses will teach you the very basics of feature engineering, like imputing variables with the mean or transforming categorical variables using one hot encoding, this course will teach you that, and much, much more. In this course, you will first learn the most popular and widely used techniques for variable engineering, like mean and median imputation, one-hot encoding, transformation with logarithm, and discretization. Then, you will discover more advanced methods that capture information while encoding or transforming your variables to improve the performance of machine learning models. You will learn methods like the weight of evidence, used in finance, and how to create monotonic relationships between variables and targets to boost the performance of linear models. You will also learn how to create features from date and time variables and how to handle categorical variables with a lot of categories. The methods that you will learn were described in scientific articles, are used in data science competitions, and are commonly utilized in organizations. And what’s more, they can be easily implemented by utilizing Python's open-source libraries! Throughout the lectures, you’ll find detailed explanations of each technique and a discussion about their advantages, limitations, and underlying assumptions, followed by the best programming practices to implement them in Python. By the end of the course, you will be able to decide which feature engineering technique you need based on the variable characteristics and the models you wish to train. And you will also be well placed to test various transformation methods and let your models decide which ones work best. Step-up your Career in Data Science You’ve taken your first steps into data science. You know about the most commonly used prediction models. You've even trained a few linear regression or classification models. At this stage, you’re probably starting to find some challenges: your data is dirty, lots of values are missing, some variables are not numerical, and others extremely skewed. You may also wonder whether your code is efficient and performant or if there is a better way to program. You search online, but you can’t find consolidated resources on feature engineering. Maybe just blogs? So you may start to wonder: how are things really done in tech companies? In this course, you will find answers to those questions. Throughout the course, you will learn multiple techniques for the different aspects of variable transformation, and how to implement them in an elegant, efficient, and professional manner using Python. You will leverage the power of Python’s open source ecosystem, including the libraries NumPy, Pandas, Scikit-learn, and special packages for feature engineering: Feature-engine and Category encoders. By the end of the course, you will be able to implement all your feature engineering steps into a single elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency. Leverage the Power of Open Source We will perform all feature engineering methods utilizing Pandas and Numpy, and we will compare the implementation with Scikit-learn, Feature-engine, and Category encoders, highlighting the advantages and limitations of each library. As you progress in the course, you will be able to choose the library you like the most to carry out your projects. There is a dedicated Python notebook with code to implement each feature engineering method, which you can reuse in your projects to speed up the development of your machine learning models. The Most Comprehensive Online Course for Feature Engineering There is no one single place to go to learn about feature engineering. It involves hours of searching on the web to find out what people are doing to get the most out of their data. That is why, this course gathers plenty of techniques used worldwide for feature transformation, learnt from data competitions in Kaggle and the KDD, scientific articles, and from the instructor’s experience as a data scientist. This course therefore provides a source of reference where you can learn new methods and also revisit the techniques and code needed to modify variables whenever you need to. This course is taught by a lead data scientist with experience in the use of machine learning in finance and insurance, who is also a book author and the lead developer of a Python open source library for feature engineering. And there is more: The course is constantly updated to include new feature engineering methods. Notebooks are regularly refreshed to ensure all methods are carried out with the latest releases of the Python libraries, so your code will never break. The course combines videos, presentations, and Jupyter notebooks to explain the methods and show their implementation in Python. The curriculum was developed over a period of four years with continuous research in the field of feature engineering to bring you the latest technologies, tools, and trends. Want to know more? Read on... This comprehensive feature engineering course contains over 100 lectures spread across approximately 10 hours of video, and ALL topics include hands-on Python code examples that you can use for reference, practice, and reuse in your own projects. REMEMBER, the course comes with a 30-day money-back guarantee, so you can sign up today with no risk. So what are you waiting for? Enrol today and join the world's most comprehensive course on feature engineering for machine learning. | https://www.udemy.com/course/feature-engineering-for-machine-learning/#instructor-1 | Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science. As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations. Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning. Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics. Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions. Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities. Feel free to contact her on LinkedIn. ======================== Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos. Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones. A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina. Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos. Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones. Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades. No dudes en contactarla en LinkedIn. | Machine Learning | Chief/Lead Role | >=4 | Below 10K | >=15K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Machine Learning Practical: 6 Real-World Applications | Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python | 4.4 | 2639 | 19333 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Rony Sulca, Ligency Team | Nov-22 | English | $9.99 | 8h 37m total length | https://www.udemy.com/course/machine-learning-practical/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | So you know the theory of Machine Learning and know how to create your first algorithms. Now what? There are tons of courses out there about the underlying theory of Machine Learning which don’t go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? Then welcome to “Machine Learning Practical”. We gathered best industry professionals with tons of completed projects behind. Each presenter has a unique style, which is determined by his experience, and like in a real world, you will need adjust to it if you want successfully complete this course. We will leave no one behind! This course will demystify how real Data Science project looks like. Time to move away from these polished examples which are only introducing you to the matter, but not giving any real experience. If you are still dreaming where to learn Machine Learning through practice, where to take real-life projects for your CV, how to not look like a noob in the recruiter's eyes, then you came to the right place! This course provides a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science. There are most exciting case studies including: ● diagnosing diabetes in the early stages ● directing customers to subscription products with app usage analysis ● minimizing churn rate in finance ● predicting customer location with GPS data ● forecasting future currency exchange rates ● classifying fashion ● predicting breast cancer ● and much more! All real. All true. All helpful and applicable. And as a final bonus: In this course we will also cover Deep Learning Techniques and their practical applications. So as you can see, our goal here is to really build the World’s leading practical machine learning course. If your goal is to become a Machine Learning expert, you know how valuable these real-life examples really are. They will determine the difference between Data Scientists who just know the theory and Machine Learning experts who have gotten their hands dirty. So if you want to get hands-on experience which you can add to your portfolio, then this course is for you. Enroll now and we’ll see you inside. | https://www.udemy.com/course/machine-learning-practical/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=15K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Data Science: Supervised Machine Learning in Python | Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Scikit-Learn | 4.7 | 2594 | 19588 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 6h 24m total length | https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Google famously announced that they are now "machine learning first", meaning that machine learning is going to get a lot more attention now, and this is what's going to drive innovation in the coming years. It's embedded into all sorts of different products. Machine learning is used in many industries, like finance, online advertising, medicine, and robotics. It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good. Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world? In this course, we are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail. It’s important to know both the advantages and disadvantages of each algorithm we look at. Next we’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability. We’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations. Next we’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice. The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning. One we’ve studied these algorithms, we’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification. We’ll do a comparison with deep learning so you understand the pros and cons of each approach. We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work. We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from. All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (for some parts) probability (continuous and discrete distributions, joint, marginal, conditional, PDF, PMF, CDF, Bayes rule) Python coding: if/else, loops, lists, dicts, sets Numpy, Scipy, Matplotlib WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/data-science-supervised-machine-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=15K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Data Science 2022 : Complete Data Science & Machine Learning | Learn and master the Data Science, Python for Machine Learning, Math for Machine Learning, Statistics for Data Science | 4.6 | 2556 | 17113 | Created by Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team | Oct-21 | English | $9.99 | 25h 48m total length | https://www.udemy.com/course/complete-data-science-and-machine-learning-using-python/ | Jitesh Khurkhuriya | Data Scientist and Digital Transformation Consultant | 4.6 | 9128 | 54212 | Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes. We are going to execute following real-life projects, Kaggle Bike Demand Prediction from Kaggle competition Automation of the Loan Approval process The famous IRIS Classification Adult Income Predictions from US Census Dataset Bank Telemarketing Predictions Breast Cancer Predictions Predict Diabetes using Prima Indians Diabetes Dataset Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills, Understanding of the overall landscape of Data Science and Machine Learning Different types of Data Analytics, Data Architecture, Deployment characteristics of Data Science and Machine Learning projects Python Programming skills which is the most popular language for Data Science and Machine Learning Mathematics for Machine Learning including Linear Algebra, Calculus and how it is applied in Machine Learning Algorithms as well as Data Science Statistics and Statistical Analysis for Data Science Data Visualization for Data Science Data processing and manipulation before applying Machine Learning Machine Learning Ridge (L2), Lasso (L1) and Elasticnet Regression/ Regularization for Machine Learning Feature Selection and Dimensionality Reduction for Machine Learning models Machine Learning Model Selection using Cross Validation and Hyperparameter Tuning Cluster Analysis for unsupervised Machine Learning Deep Learning using most popular tools and technologies of today. This Data Science and Machine Learning course has been designed considering all of the above aspects, the true Data Science and Machine Learning A-Z Course. In many Data Science and Machine Learning courses, algorithms are taught without teaching Python or such programming language. However, it is very important to understand the construct of the language in order to implement any discipline including Data Science and Machine Learning. Also, without understanding the Mathematics and Statistics it's impossible to understand how some of the Data Science and Machine Learning algorithms and techniques work. Data Science and Machine Learning is a complex set of topics which are interlinked. However, we firmly believe in what Einstein once said, "If you can not explain it simply, you have not understood it enough." As an instructor, I always try my level best to live up to this principle. This is one comprehensive course on Data Science and Machine Learning that teaches you everything required to learn Data Science and Machine Learning using the simplest examples with great depth. As you will see from the preview lectures, some of the most complex topics are explained in a simple language. Some of the key skills you will learn, Python Programming Python has been ranked as the #1 language for Data Science and Machine Learning. It is easy to use and is rich with various libraries and functions required for performing various tasks for Data Science and Machine Learning. Moreover, it is the most preferred and default language of use for many Deep Learning frameworks including Tensorflow and Keras. Advance Mathematics for Machine Learning Mathematics is the very basis for Data Science in general and Machine Learning in particular. Without understanding the meanings of Vectors, Matrices, their operations as well as understanding Calculus, it is not possible to understand the foundation of the Data Science and Machine Learning. Gradient Descent which forms the very basis of Neural Network and Machine Learning is built upon the basics of Calculus and Derivatives. Advance Statistics for Data Science It is not enough to know only mean, median, mode etc. The advance techniques of Data Science and Machine Learning such as Feature Selection, Dimensionality Reduction using PCA are all based on advance inferential statistics of Distributions and Statistical Significance. It also helps us understanding the data behavior and then apply an appropriate machine learning technique to get the best result from various techniques of Data Science and Machine Learning. Data Visualization As they say, picture is worth a thousand words. Data Visualization is one of the key techniques of Data Science and Machine Learning and is used for Exploratory Data Analysis. In that, we visually analyse the data to identify the patterns and trends. We are going to learn how to create various plots and charts as well as how to analyse them for all the practical purposes. Feature Selection plays a key role in Machine Learning and Data Visualisation is key for it. Data Processing Data Science require extensive data processing. Data Science and Machine Learning practitioners spend more than 2/3rd of the time processing and analysing the data. Data can be noisy and is never in the best shape and form. Data Processing is one of the key disciplines of Data Science and Machine Learning to get the best results. We will be using Pandas which is the most popular library for data processing in Python and various other libraries to read, analyse, process and clean the data. Machine Learning The heart and soul of Data Science is the predictive ability provided by the algorithms from Machine Learning and Deep Learning. Machine Learning takes the overall discipline of Data Science ahead of others. We will combine everything we would learn from the previous sections and build various machine learning models. The key aspects of the Machine Learning is not just about the algorithms but also understanding various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values impact the outcome so that you can build the best machine learning models. Feature Selection and Dimensionality Reduction In case you wonder, what makes a good data scientists, then this section is the answer. A good Data Science and Machine Learning practitioner does not just use libraries and code few lines. She will analyse every feature of the data objectively and choose the most relevant ones based on statistical analysis. We will learn how to reduce the number of features as well as how we can retain the value in the data when we practice and build various machine learning models after applying the principles of Feature Selection and Dimensionality Reduction using PCA. Deep Learning You can not become a good Data Science and Machine Learning practitioner, if you do not know how to build powerful neural network. Deep Learning can be said to be another kind of Machine Learning with great power and flexibility. After Learning Machine Learning, we are going to learn some key fundamentals of Deep Learning and build a solid foundation first. We will then use Keras and Tensorflow which are the most popular Deep Learning frameworks in the world. Kaggle Project As an aspiring Data Scientists, we always wish to work on Kaggle project for Machine Learning and achieve good results. I have spent huge effort and time in making sure you understand the overall process of performing a real Data Science and Machine Learning project. This is going to be a good Machine Learning challenge for you. Your takeaway from this course, Complete hands-on experience with huge number of Data Science and Machine Learning projects and exercises Learn the advance techniques used in the Data Science and Machine Learning Certificate of Completion for the most in demand skill of Data Science and Machine Learning All the queries answered in shortest possible time. All future updates based on updates to libraries, packages Continuous enhancements and addition of future Machine Learning course material All the knowledge of Data Science and Machine Learning at fraction of cost This Data Science and Machine Learning course comes with the Udemy's 30-Day-Money-Back Guarantee with no questions asked. So what you are waiting for? Hit the "Buy Now" button and get started on your Data Science and Machine Learning journey without spending much time. I am so eager to see you inside the course. Disclaimer: All the images used in this course are either created or purchased/downloaded under the license from the provider, mostly from Shutterstock or Pixabay. | https://www.udemy.com/course/complete-data-science-and-machine-learning-using-python/#instructor-1 | Jitesh has over 20 years of technology experience and worked as programmer, Product Head as well as the Data Scientist. Jitesh has worked with various fortune 500 companies and governments across the world. As the Data Scientist and Anti-Fraud Expert, he was the member of the high-profile team to suggest tax reforms and amendments in VAT, Customs and Income Tax based on fraud pattern analysis, countrywide data mining and analysis, business process security analysis. This not only contributed to a revolutionary change in the tax processes but also reduced the tax and customs frauds. As a seasoned leader in Digital Transformation, Jitesh has developed and executed strategies that generated high top and bottom line revenue streams. | Machine Learning | Consultant | >=4 | Below 10K | >=15K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Deep Learning: GANs and Variational Autoencoders | Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow | Bestseller | 4.6 | 2533 | 24187 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 7h 46m total length | https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. What is unsupervised learning? Unsupervised learning means we’re not trying to map input data to targets, we’re just trying to learn the structure of that input data. Once we’ve learned that structure, we can do some pretty cool things. One example is generating poetry - we’ve done examples of this in the past. But poetry is a very specific thing, how about writing in general? If we can learn the structure of language, we can generate any kind of text. In fact, big companies are putting in lots of money to research how the news can be written by machines. But what if we go back to poetry and take away the words? Well then we get art, in general. By learning the structure of art, we can create more art. How about art as sound? If we learn the structure of music, we can create new music. Imagine the top 40 hits you hear on the radio are songs written by robots rather than humans. The possibilities are endless! You might be wondering, "how is this course different from the first unsupervised deep learning course?" In this first course, we still tried to learn the structure of data, but the reasons were different. We wanted to learn the structure of data in order to improve supervised training, which we demonstrated was possible. In this new course, we want to learn the structure of data in order to produce more stuff that resembles the original data. This by itself is really cool, but we'll also be incorporating ideas from Bayesian Machine Learning, Reinforcement Learning, and Game Theory. That makes it even cooler! Thanks for reading and I’ll see you in class. =) "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Calculus Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent Know how to build a feedforward and convolutional neural network in Theano or TensorFlow WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/deep-learning-gans-and-variational-autoencoders/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=20K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Careers in Data Science A-Z™ | How to Become a Top Level Data Scientist - Learn What to Expect, How to be Prepared, How to Stand Out and More... | 4.7 | 2493 | 12521 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Nov-22 | English | $9.99 | 3h 34m total length | https://www.udemy.com/course/careers-in-data-science-a-ztm/ | Kirill Eremenko | Data Scientist | 4.5 | 597584 | 2241495 | Becoming a Data Scientist might be on your mind right now. Named the "Sexiest Job of the 21st Century", this career seems like a great idea not only due to its high demand, but lack of supply of skilled proffesionals. But the million dollar question is: What makes the difference between Top Level Data Scientist and just another one from the bunch? Here is where this course jumps in... With over 8 years combined experience in the field, we've decided to step back and put all of the lessons we've learned through our careers into one simple course. If you want to get valuable insights, advice, hacks & tips, recommendations, lessons from failures and successes from our careers and learn how to apply it to your own and take your Data Science career to the next level, then this course is just for you. | https://www.udemy.com/course/careers-in-data-science-a-ztm/#instructor-1 | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | Below 10K | >=10K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Python for Statistical Analysis | Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries | 4.5 | 2490 | 53060 | Created by Samuel Hinton, Ligency I Team, Ligency Team | Jul-22 | English | $9.99 | 8h 38m total length | https://www.udemy.com/course/python-for-statistical-analysis/ | Samuel Hinton | Astrophysicist, Software Engineer and Presenter | 4.5 | 4146 | 88236 | Welcome to Python for Statistical Analysis! This course is designed to position you for success by diving into the real-world of statistics and data science. Learn through real-world examples: Instead of sitting through hours of theoretical content and struggling to connect it to real-world problems, we'll focus entirely upon applied statistics. Taking theory and immediately applying it through Python onto common problems to give you the knowledge and skills you need to excel. Presentation-focused outcomes: Crunching the numbers is easy, and quickly becoming the domain of computers and not people. The skills people have are interpreting and visualising outcomes and so we focus heavily on this, integrating visual output and graphical exploration in our workflows. Plus, extra bonus content on great ways to spice up visuals for reports, articles and presentations, so that you can stand out from the crowd. Modern tools and workflows: This isn't school, where we want to spend hours grinding through problems by hand for reinforcement learning. No, we'll solve our problems using state-of-the-art techniques and code libraries, utilising features from the very latest software releases to make us as productive and efficient as possible. Don't reinvent the wheel when the industry has moved to rockets. | https://www.udemy.com/course/python-for-statistical-analysis/#instructor-1 | Hi, I'm Sam and I'm an astrophysicist, data scientist, robotics and software engineer, astronomer and public presenter. My work right now is all about renewable energy. Battery assets, optimising their utilisation and trading energy in markets to cut out as many fossil fuel generators as humanly possible. In academia, my primary work involves investigating the nature of dark energy, however I also spend a lot of time advocating of open-source development and proper coding practices. With years of experience from the financial software industry to machine learning pipelines classifying objects in the night sky, and teaching experience in statistics, software engineering, data manipulation, computational physics, and much more, I'm dedicated to increasing the level of coding proficiency in the scientific fields, and bringing basic coding knowledge to any eager student. On top of my research work, I've run national coding workshops with content ranging from complete novices up to research experts. I'm excited to bring my knowledge and content to a wider audience, and hope that my direct and to-the-point teaching attitude allows students to understand the core concepts faster and better, saving students time and stress! | Python | Engineer/Developer | >=4 | Below 10K | >=50K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Introduction to Time Series Analysis and Forecasting in R | Work with time series and all sorts of time related data in R - Forecasting, Time Series Analysis, Predictive Analytics | Bestseller | 4.4 | 2478 | 12805 | Created by R-Tutorials Training | Mar-19 | English | $11.99 | 8h 32m total length | https://www.udemy.com/course/time-series-analysis-and-forecasting-in-r/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263421 | Understand the Now – Predict the Future! Time series analysis and forecasting is one of the key fields in statistical programming. It allows you to see patterns in time series datamodel this datafinally make forecasts based on those models Due to modern technology the amount of available data grows substantially from day to day. Successful companies know that. They also know that decisions based on data gained in the past, and modeled for the future, can make a huge difference. Proper understanding and training in time series analysis and forecasting will give you the power to understand and create those models. This can make you an invaluable asset for your company/institution and will boost your career! What will you learn in this course and how is it structured? You will learn about different ways in how you can handle date and time data in R. Things like time zones, leap years or different formats make calculations with dates and time especially tricky for the programmer. You will learn about POSIXt classes in R Base, the chron package and especially the lubridate package. You will learn how to visualize, clean and prepare your data. Data preparation takes a huge part of your time as an analyst. Knowing the best functions for outlier detection, missing value imputation and visualization can safe your day. After that you will learn about statistical methods used for time series. You will hear about autocorrelation, stationarity and unit root tests. Then you will see how different models work, how they are set up in R and how you can use them for forecasting and predictive analytics. Models taught are: ARIMA, exponential smoothing, seasonal decomposition and simple models acting as benchmarks. Of course all of this is accompanied with plenty of exercises. Where are those methods applied? In nearly any quantitatively working field you will see those methods applied. Especially econometrics and finance love time series analysis. For example stock data has a time component which makes this sort of data a prime target for forecasting techniques. But of course also in academia, medicine, business or marketing techniques taught in this course are applied. Is it hard to understand and learn those methods? Unfortunately learning material on Time Series Analysis Programming in R is quite technical and needs tons of prior knowledge to be understood. With this course it is the goal to make understanding modeling and forecasting as intuitive and simple as possible for you. While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with time data on a regular basis can benefit from this course. How do I prepare best to benefit from this course? It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (course R Basics). What R you waiting for? | https://www.udemy.com/course/time-series-analysis-and-forecasting-in-r/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Misc | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Data Science Career Guide - Interview Preparation | Prepare for your Data Science Interview with this full guide on a career in Data Science including practice questions! | Bestseller | 4.6 | 2412 | 19688 | Created by Jose Portilla | Sep-19 | English | $9.99 | 4h 0m total length | https://www.udemy.com/course/data-science-career-guide-interview-preparation/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | According to Glassdoor, a career as a Data Scientist is the best job in America! With an average base salary of over $120,000, not only do Data Scientists earn fantastic compensation, but they also get to work on some of the world's most interesting problems! Data Scientist positions are also rated as having some of the best work-life balances by Glassdoor. Companies are in dire need of filling out this unique role, and you can use this course to help you rock your Data Scientist Interview! This course is designed to be the ultimate resource for getting a career as a Data Scientist. We'll start off with an general overview of the field and discuss multiple career paths, including Product Analyst, Data Engineering, Data Scientist, and many more. You'll understand the various opportunities available and the best way to pursue each of them. The course touches upon a wide variety of topics, including questions on probability, statistics, machine learning, product metrics, example data sets, A/B testing, market analysis, and much more! The course will be full of real questions sourced from employees working at some of the world's top technology companies, including Amazon, Square, Facebook, Google, Microsoft, AirBnb and more! The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. For questions requiring coded solutions, fully commented code examples will be shown for both Python and R. This way you can focus on understanding the code in a programming language you're already familiar with, instead of worrying about syntax! | https://www.udemy.com/course/data-science-career-guide-interview-preparation/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Misc | Head/Director | >=4 | Below 10K | >=15K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
The Beginner's Guide to Artificial Intelligence (Unity 2022) | A practical guide to programming non-player characters for games in the Unity Game Engine with C# | 4.7 | 2260 | 38020 | Created by Penny de Byl, Penny @Holistic3D.com | Aug-22 | English | $10.99 | 30h 10m total length | https://www.udemy.com/course/artificial-intelligence-in-unity/ | Penny de Byl | International Award Winning Professor & Best Selling Author | 4.6 | 21114 | 138779 | Do your non-player characters (NPCs) lack drive and ambition? Are they slow, stupid and constantly banging their heads against the wall? Then this course is for you. Join Penny as she explains, demonstrates and assists you in creating your very own NPCs in Unity with C#. All you need is a sound knowledge of Unity, C# and the ability to add two numbers together. This course uses Unity Version 2021.3 LTS In this course, Penny reveals the most popular AI techniques used for creating believable character behaviour in games using her internationally acclaimed teaching style and knowledge from over 30 years working with games, graphics and having written two award winning books on games AI. Throughout, you will follow along with hands-on workshops designed to teach you about the fundamental AI techniques used in today's games. You'll join in as NPCs are programmed to chase, patrol, shoot, race, crowd and much more. Learn how to program and work with: vectors waypoints navmeshes the A* algorithm crowds flocks animated characters vehicles and industry standard techniques such as goal-oriented action learning and behaviour trees. Contents and Overview The course begins with a detailed examination of vector mathematics that sits at the very heart of programming the movement of NPCs. Following this, systems of waypoints will be used to move characters around in an environment before examining the Unity waypoint system for car racing with AI controlled cars. This leads into an investigation of graph theory and the A* algorithm before we apply these principles to developing navmeshes and developing NPCs who can find their way around a game environment. Before an aquarium is programmed complete with autonomous schooling fish, crowds of people will be examined from the recreation of sidewalk traffic, to groups of people fleeing from danger. Having examined the differing ways to move NPCs around in a game environment, their thinking abilities will be discussed with full explanations and more hands-on workshops using finite state machines and behaviour trees. The follow-along workshops included in the course come with starter Unity asset files and projects complete with solutions. Throughout, there are also quizzes and challenge exercises to reinforce your learning and guide you to express your newfound knowledge. At the completion of this course you will have gained a broad understanding of what AI is in games, how it works and how you can use it in your own projects. It will equip you with a toolset to examine any of the techniques presented in more depth to take your game environments to the next level. What students are saying about this course: This has been my favourite Udemy-Unity course so far. It took me from literally 0% knowledge of how game AI is achieved, and took me to a whole new level. Waypoints, pathfinding, state machines, etc etc etc are all covered in-depth and will reveal the magic (spoiler alert: it isn't magic) behind making your computer characters seem like they really have a mind of their own. Oh My God. I love her way of teaching things. I haven’t finished this course yet. But all i can say is that it is another brilliant course from her. Artificial intelligence by itself is a tricky thing to do. And before starting this course i never thought that i will understand anything in it. But i was wrong. With her style of teaching, you will learn how to move your characters in an ”intelligent“ way. This course is perfectly sliced and the pace is wonderful. | https://www.udemy.com/course/artificial-intelligence-in-unity/#instructor-1 | Hi, I'm Dr Penny de Byl. I'm a full stack developer of most things computer sciency and academic with a true passion for teaching. I've been teaching others about games development, programming, computer graphics, animation and web design for over 25 years in universities in Australia and Europe at the full professor level. I've also consulted for Unity, SAE, the Australian Institute of Entertainment and Wikitude. My best selling textbooks including Holistic Game Development with Unity are used in over 100 institutions world-wide. My graduates work at companies like Apple, Ubisoft, LinkedIn and Deloitte Digital. I have an honours degree in computer graphics and a Ph.D. in artificial intelligence for games characters. Over the course of my career I've won numerous awards for teaching excellence at the state, national and international levels including the Australian Learning and Teaching Council's Excellence in Teaching Award and the Unity Mobile Game Curriculum Competition. My approach to teaching computer science and related fields is project-based giving you hands-on workshops you can immediately get your teeth into. I want you to leave my virtual classroom fully armed with a toolkit of skills for life-long learning. I'm excited to now be focussing my efforts full-time on Udemy to bring my years of knowledge and experience to those eager to learn about technology. | Artificial Intelligence | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=35K | >=4 | Below 1 Lakh | >=1 Lakh | |||||||||||||||||
Complete Machine Learning with R Studio - ML for 2022 | Linear & Logistic Regression, Decision Trees, XGBoost, SVM & other ML models in R programming language - R studio | 4.6 | 2232 | 254755 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 11h 59m total length | https://www.udemy.com/course/machine-learning-with-r-studio/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Machine Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, R and Predictive Modeling, right? You've found the right Machine Learning course! After completing this course, you will be able to: · Confidently build predictive Machine Learning models using R to solve business problems and create business strategy · Answer Machine Learning related interview questions · Participate and perform in online Data Analytics competitions such as Kaggle competitions Check out the table of contents below to see what all Machine Learning models you are going to learn. How will this course help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning, R and predictive modelling in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, R and predictive modelling. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an in-depth understanding of machine learning and predictive modelling techniques using R. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques using R, Python, and we have used our experience to include the practical aspects of data analysis in this course. We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, machine learning, R, predictive modelling, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts of machine learning, R and predictive modelling. Each section contains a practice assignment for you to practically implement your learning on machine learning, R and predictive modelling. Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use R for Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R than Python. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. As compared to Python, R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, usage of R and Python has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Like Python, adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What are the major advantages of using R over Python? As compared to Python, R has a higher user base and the biggest number of statistical packages and libraries available. Although, Python has almost all features that analysts need, R triumphs over Python. R is a function-based language, whereas Python is object-oriented. If you are coming from a purely statistical background and are not looking to take over major software engineering tasks when productizing your models, R is an easier option, than Python. R has more data analysis functionality built-in than Python, whereas Python relies on Packages Python has main packages for data analysis tasks, R has a larger ecosystem of small packages Graphics capabilities are generally considered better in R than in Python R has more statistical support in general than Python What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-with-r-studio/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Machine Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Deep Learning with TensorFlow 2.0 [2022] | Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case | 4.6 | 2218 | 19863 | Created by 365 Careers, 365 Careers Team | Nov-20 | English | $14.99 | 5h 55m total length | https://www.udemy.com/course/machine-learning-with-tensorflow-for-business-intelligence/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common? They are all masters of deep learning. We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all. Cool, that sounds like a really important skill; how do I become a Master of Deep Learning? There are two routes you can take: The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there. The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks. Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else? Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance. We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more. Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge optimizations, get hands on with TensorFlow and even build your very own algorithm and put it through training! Wow, that’s going to look great on your resume! Speaking of resumes, you also get a certificate upon completion which employers can verify that you have successfully finished a prestigious 365 Careers course – and one of our best at that! Now, I can see you’re bragging a little, but I admit you have peaked my interest. What else does your course offer that will make my resume shine? Trust us, after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview. Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms. Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc. Understand the backpropagation process, intuitively and mathematically. You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learning Get to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too Learn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included! Also, I don’t know if we’ve mentioned this, but you will have created your very own Deep Learning Algorithm after only 1 hour of the course. It’s this hands-on experience that will really make your resume stand out This all sounds great, but I am a little overwhelmed, I’m afraid I may not have enough experience. We admit, you will need at least a little understanding of Python programming but nothing to worry about. We start with the basics and take you step by step toward building your very first (or second, or third etc.) Deep Learning algorithm – we program everything in Python and explain each line of code. We do this early on and it will give you the confidence to carry on to the more complex topics we cover. All the sophisticated concepts we teach are explained intuitively. Our beautifully animated videos and step by step approach ensures the course is a fun and engaging experience for all levels. We want everyone to get the most out of our course, and the best way to do that is to keep our students motivated. So, we worked hard to ensure that students with varying skills are challenged without being overwhelmed. Each lecture builds upon the last and practical exercises mean that you can practice what you’ve learned before moving on to the next step. And of course, we are available to answer any queries you have. In fact, we aim to answer any and all question within 1 business day. We don’t just chuck you in the pool then head to the bar and let you fend for yourself. Remember, we don’t just want you to enrol – we want you to complete the course and become a Master of Deep Learning. OK, awesome! I feel much better about my level of experience now, but we haven’t discussed yours! How do I know you can teach me to become a Master of Deep Learning? That’s an understandable worry, but it’s one we have no problem removing. We are 365 Careers and we’ve been creating online courses for ages. We have over 1,750,000 students and enjoy high ratings for all our Udemy courses. We are a team of experts who are all, at heart, teachers. We believe knowledge should be shared and not just through boring text books but in engaging and fun ways. We are well aware how difficult it is to build your knowledge and skills in the data science field, it’s so new and has grown so fast that the education sector has struggled to keep up and offer any substantial methods of teaching these topic areas. We wanted to change things – to rock the boat – so we developed our unique teaching style, one that countless students have enjoyed and thrived with. And between us, we think this course is one of our favourites, so if this is your first time with us, you’re in for a treat. If it’s not and you’ve taken one of our courses before, then, you’re still in for a treat! I’ve been hurt before though, how can I be sure you won’t let me down? Easy, with Udemy’s 30-day money back guarantee. We strive for the best and believe that our courses are the best out there. But you know what, everyone is different, and we understand that. So, we have no problem offering this guarantee, we want students who will complete and get the most out of this course. If you are one of the few who finds this course not what you wanted or expected then, get your money back. No questions, no risk, no problem. Great, that takes a load of my shoulders. What next? Click on the ‘Buy now’ button and take that first step toward a satisfying data science career and becoming a Master of Deep Learning. | https://www.udemy.com/course/machine-learning-with-tensorflow-for-business-intelligence/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Deep Learning | >=4 | Below 10K | >=15K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||||
Unsupervised Deep Learning in Python | Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA | 4.8 | 2076 | 19341 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $34.99 | 10h 10m total length | https://www.udemy.com/course/unsupervised-deep-learning-in-python/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning! In these course we’ll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA. Last, we’ll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I’ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD-k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity. Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found. All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and Python coding. You'll want to install Numpy, Theano, and Tensorflow for this course. These are essential items in your data analytics toolbox. If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus linear algebra probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file can write a feedforward neural network in Theano or Tensorflow WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) | https://www.udemy.com/course/unsupervised-deep-learning-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Deep Learning | Engineer/Developer | >=4 | Below 10K | >=15K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Ensemble Machine Learning in Python: Random Forest, AdaBoost | Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python | 4.7 | 1905 | 14319 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $29.99 | 5h 39m total length | https://www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years. Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics. It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good. Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world? This course is all about ensemble methods. We've already learned some classic machine learning models like k-nearest neighbor and decision tree. We've studied their limitations and drawbacks. But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor? In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. In particular, we will study the Random Forest and AdaBoost algorithms in detail. To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously. We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are. Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks. All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Calculus (derivatives) Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Simple machine learning models like linear regression and decision trees WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/machine-learning-in-python-random-forest-adaboost/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Machine Learning- From Basics to Advanced | A beginners guide to learn Machine Learning (including Hands-on projects - From Basic to Advance Level) | 4.2 | 1887 | 273771 | Created by EdYoda Digital University, Awantik Das | Apr-20 | English | $9.99 | 6h 49m total length | https://www.udemy.com/course/step-by-step-guide-to-machine-learning-course/ | EdYoda Digital University | Visit us at www.edyoda.com | 4.2 | 37880 | 1018982 | If you are looking to start your career in Machine learning then this is the course for you. This is a course designed in such a way that you will learn all the concepts of machine learning right from basic to advanced levels. This course has 5 parts as given below: Introduction & Data Wrangling in machine learning Linear Models, Trees & Preprocessing in machine learning Model Evaluation, Feature Selection & Pipelining in machine learning Bayes, Nearest Neighbors & Clustering in machine learning SVM, Anomalies, Imbalanced Classes, Ensemble Methods in machine learning For the code explained in each lecture, you can find a GitHub link in the resources section. Who's teaching you in this course? I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies - PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan - Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS - Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML. Machine learning is the fuel we need to power robots, alongside AI. With Machine Learning, we can power programs that can be easily updated and modified to adapt to new environments and tasks to get things done quickly and efficiently. Here are a few reasons for you to pursue a career in Machine Learning: 1) Machine learning is a skill of the future – Despite the exponential growth in Machine Learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in Machine Learning, you will have a secure career in a technology that is on the rise. 2) Work on real challenges – Businesses in this digital age face a lot of issues that Machine learning promises to solve. As a Machine Learning Engineer, you will work on real-life challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives high satisfaction. 3) Learn and grow – Since Machine Learning is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer. 4) An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have the experience and expertise to follow an upward career graph and approach your ideal employers. 5) Build a lucrative career– The average salary of a Machine Learning engineer is one of the top reasons why Machine Learning seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by. 6) Side-step into data science – Machine learning skills help you expand avenues in your career. Machine Learning skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge. Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise. Enroll Now!! See You in Class. Happy learning Team Edyoda | https://www.udemy.com/course/step-by-step-guide-to-machine-learning-course/#instructor-1 | EdYoda is re-imagining skill based education, educating on job-relevant real world skills. Edyoda courses are on job-relevant technical skills. We have professional team of instructors, some of the courses we specialize in are Web development, Mobile App Development, Cloud & DevOps, Machine Learning, Artificial Intelligence and Big Data. We believe that access to education and opportunities is the biggest enabler and we are on a mission to enable the same for everyone across the world. | Machine Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Statistics for Data Analysis Using R | Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science | 4.7 | 1862 | 10046 | Created by Sandeep Kumar, Quality Gurus Inc. | Sep-21 | English | $11.99 | 12h 25m total length | https://www.udemy.com/course/statistics-using-r/ | Sandeep Kumar, Quality Gurus Inc. | Experienced Quality Director • Six Sigma Coach • Consultant | 4.5 | 49014 | 221169 | Perform simple or complex statistical calculations using R Programming! - You don't need to be a programmer for this 🙂 Learn statistics, and apply these concepts in your workplace using R. The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data sets are used to explain the application. I will explain the basic theory first, and then I will show you how to use R to perform these calculations. The following areas of statistics are covered: Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package) Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands) Probability - Basic Concepts, Permutations, Combinations (Basic theory only) Population and Sampling - Basic concepts (theory only) Probability Distributions - Normal, Binomial and Poisson Distributions (Base R functions and the visualize package) Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test, Chi-Square Test ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R. What are other students saying about this course? This course is a perfect mix of theory and practice. I highly recommend it for those who want to not only get good with R, but to also become proficient in statistics. (5 stars by Aaron Verive) You get both the “how” and “why” for both the statistics and R programming. I’m really happy with this course. (5 stars by Elizabeth Crook) Sandeep has such a clear approach, pedagogic and explains everything he does. Perfect for a novice like myself. (5 stars by Hashim Al-Haboobi) Very clear explanation. Coming from a non-technical background, it is immensely helpful that Prof. Sandeep Kumar is explaining all the minor details to prevent any scope for confusion. (5 stars by Ann Mary Biju) I had a limited background in R and statistics going into this course. I feel like this gave me the perfect foundation to progress to more complex topics in both of those areas. I'm very happy I took this course. (5 stars by Thach Phan) Dr. Kumar is a fantastic teacher who takes you step by step. Can't say enough about his approach. Detailed. Not only clear descriptions of statistics but you will learn many details that make R easier to use and understand. (5 stars by James Reynolds) This is a wonderful course, I do recommend it. The best Udemy course I took. (5 stars by Joao Alberto Arantes Do Amaral) The course exceeded my expectations and i would like to thank the instructor Mr Sandeep Kumar for creating such an amazing course. The best thing about this course is the Theory incorporated that helps you understand what you are going to code in R. I have really learnt a lot. If you a looking for the best course for R then look no further because this is the best there can be. (5 stars by Kipchumba Brian) What are you waiting for? This course comes with Udemy's 30 days money-back guarantee. If you are not satisfied with the course, get your money back. I hope to see you in the course. | https://www.udemy.com/course/statistics-using-r/#instructor-1 | ASQ ConnEx Expert, PMI-PMP, IRCA Registered Lead Auditor, ASQ - CSSBB, CQA, CQE, CMQ/OE, IIA - CIA, NAHQ - CPHQ Sandeep Kumar has more than 35 years of Quality Management experience. He has worked as Quality Director on several projects, including Power, Oil and Gas and Infrastructure projects. In addition, he provides coaching and consulting services to implement Lean Six Sigma to improve performance. After the successful completion of ASQ vetting, Sandeep Kumar has been designated as a genuine and authorized ASQConnEx expert. ASQConnEx is an education delivery system and network that vets, designates, and connects quality subject matter experts with organizations to advance their excellence journey. His areas of specialization include Quality Assurance, ISO 9001:2015, Lean, Six Sigma, Risk Management, QMS Audits, Supplier Quality Surveillance, Supplier Pre-qualification, Construction Quality, Mechanical Inspection and Quality Training. Professional Qualifications: His professional qualifications/certifications include: • Authorized ASQ ConnEx Expert • ASQ-CSSBB, Certified Six Sigma Black Belt • ASQ-CMQ/OE Certified Manager of Quality/Organizational Excellence • PMI-PMP Certified Project Management Professional • IRCA Registered Lead Auditor (QMS-2015) • IIA-CIA Certified Internal Auditor • NAHQ-CPHQ Certified Professional in Healthcare Quality • ASQ-CSSGB, Certified Six Sigma Green Belt • ASQ-CQA Certified Quality Auditor • ASQ-CQE Certified Quality Engineer • ASQ-CSQP Certified Supplier Quality Professional | Statistics | Head/Director | Yes | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||
A Complete Guide on TensorFlow 2.0 using Keras API | Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0 | 4.4 | 1842 | 53166 | Created by Hadelin de Ponteves, Ligency I Team, Luka Anicin, Ligency Team | Nov-22 | English | $11.99 | 13h 5m total length | https://www.udemy.com/course/tensorflow-2/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1624105 | Welcome to Tensorflow 2.0! TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop. Deep Learning is one of the fastest growing areas of Artificial Intelligence. In the past few years, we have proven that Deep Learning models, even the simplest ones, can solve very hard and complex tasks. Now, that the buzz-word period of Deep Learning has, partially, passed, people are releasing its power and potential for their product improvements. The course is structured in a way to cover all topics from neural network modeling and training to put it in production. In Part 1 of the course, you will learn about the technology stack that we will use throughout the course (Section 1) and the TensorFlow 2.0 library basics and syntax (Section 2). In Part 2 of the course, we will dig into the exciting world of deep learning. Through this part of the course, you will implement several types of neural networks (Fully Connected Neural Network (Section 3), Convolutional Neural Network (Section 4), Recurrent Neural Network (Section 5)). At the end of this part, Section 6, you will learn and build their own Transfer Learning application that achieves state of the art (SOTA) results on the Dogs vs. Cats dataset. After passing the part 2 of the course and ultimately learning how to implement neural networks, in Part 3 of the course, you will learn how to make your own Stock Market trading bot using Reinforcement Learning, specifically Deep-Q Network. Part 4 is all about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and create your own data pipelines for production. In Section 8 we will check if the dataset has any anomalies using the TensorFlow Data Validation library and after learn how to check a dataset for anomalies, in Section 9, we will make our own data preprocessing pipeline using the TensorFlow Transform library. In Section 10 of the course, you will learn and create your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section, you will get a better picture of how to send a request to a model over the internet. However, at this stage, the architecture around the model is not scalable to millions of request. Enter the Section 11. In this section of the course, you will learn how to improve solution from the previous section by using the TensorFlow Serving library. In a very easy way, you will learn and create your own Image Classification API that can support millions of requests per day! These days it is becoming more and more popular to have a Deep Learning model inside an Android or iOS application, but neural networks require a lot of power and resources! That's where the TensorFlow Lite library comes into play. In Section 12 of the course, you will learn how to optimize and convert any neural network to be suitable for a mobile device. To conclude with the learning process and the Part 5 of the course, in Section 13 you will learn how to distribute the training of any Neural Network to multiple GPUs or even Servers using the TensorFlow 2.0 library. | https://www.udemy.com/course/tensorflow-2/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Tensor Flow | Founder/Entrepreneur | >=4 | Below 10K | >=50K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
A Beginner's Guide To Machine Learning with Unity | Advanced games AI with genetic algorithms, neural networks & Q-learning in C# and Tensorflow for Unity | 4.5 | 1839 | 21817 | Created by Penny de Byl, Penny @Holistic3D.com | Aug-21 | English | $10.99 | 13h 3m total length | https://www.udemy.com/course/machine-learning-with-unity/ | Penny de Byl | International Award Winning Professor & Best Selling Author | 4.6 | 21114 | 138779 | What if you could build a character that could learn while it played? Think about the types of gameplay you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course, we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics. In addition she's written two award winning books on games AI and two others best sellers on Unity game development. Throughout the course you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques, distilling the mathematics in a way that the topic becomes accessible to the most noob of novices. Learn how to program and work with: genetic algorithms neural networks human player captured training sets reinforcement learning Unity's ML-Agent plugin Tensorflow Contents and Overview The course starts with a thorough examination of genetic algorithms that will ease you into one of the simplest machine learning techniques that is capable of extraordinary learning. You'll develop an agent that learns to camouflage, a Flappy Bird inspired application in which the birds learn to make it through a maze and environment-sensing bots that learn to stay on a platform. Following this, you'll dive right into creating your very own neural network in C# from scratch. With this basic neural network, you will find out how to train behaviour, capture and use human player data to train an agent and teach a bot to drive. In the same section you'll have the Q-learning algorithm explained, before integrating it into your own applications. By this stage, you'll feel confident with the terminology and techniques used throughout the deep learning community and be ready to tackle Unity's experimental ML-Agents. Together with Tensorflow, you'll be throwing agents in the deep-end and reinforcing their knowledge to stay alive in a variety of game environment scenarios. By the end of the course, you'll have a well-equipped toolset of basic and solid machine learning algorithms and applications, that will see you able to decipher the latest research publications and integrate the latest developments into your work, while keeping abreast of Unity's ML-Agents as they evolve from experimental to production release. What students are saying about this course: Absolutely the best beginner to Advanced course for Neural Networks/ Machine Learning if you are a game developer that uses C# and Unity. BAR NONE x Infinity. A perfect course with great math examples and demonstration of the TensorFlow power inside Unity. After this course, you will get the strong basic background in the Machine Learning. The instructor is very engaging and knowledgeable. I started learning from the first lesson and it never stopped. If you are interested in Machine Learning , take this course. | https://www.udemy.com/course/machine-learning-with-unity/#instructor-1 | Hi, I'm Dr Penny de Byl. I'm a full stack developer of most things computer sciency and academic with a true passion for teaching. I've been teaching others about games development, programming, computer graphics, animation and web design for over 25 years in universities in Australia and Europe at the full professor level. I've also consulted for Unity, SAE, the Australian Institute of Entertainment and Wikitude. My best selling textbooks including Holistic Game Development with Unity are used in over 100 institutions world-wide. My graduates work at companies like Apple, Ubisoft, LinkedIn and Deloitte Digital. I have an honours degree in computer graphics and a Ph.D. in artificial intelligence for games characters. Over the course of my career I've won numerous awards for teaching excellence at the state, national and international levels including the Australian Learning and Teaching Council's Excellence in Teaching Award and the Unity Mobile Game Curriculum Competition. My approach to teaching computer science and related fields is project-based giving you hands-on workshops you can immediately get your teeth into. I want you to leave my virtual classroom fully armed with a toolkit of skills for life-long learning. I'm excited to now be focussing my efforts full-time on Udemy to bring my years of knowledge and experience to those eager to learn about technology. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=20K | >=4 | Below 1 Lakh | >=1 Lakh | |||||||||||||||||
Feature Selection for Machine Learning | Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more. | 4.8 | 1824 | 12952 | Created by Soledad Galli | Jun-22 | English | $12.99 | 5h 49m total length | https://www.udemy.com/course/feature-selection-for-machine-learning/ | Soledad Galli | Lead Data Scientist | 4.5 | 10170 | 46124 | Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online. In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models. Who is this course for? You’ve given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables. At this stage you’ve probably realized that many data sets contain an enormous amount of features, and some of them are identical or very similar, some of them are not predictive at all, and for some others it is harder to say. You wonder how you can go about to find the most predictive features. Which ones are OK to keep and which ones could you do without? You also wonder how to code the methods in a professional manner. Probably you did your online search and found out that there is not much around there about feature selection. So you start to wonder: how are things really done in tech companies? This course will help you! This is the most comprehensive online course in variable selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features. What will you learn? I have put together a fantastic collection of feature selection techniques, based on scientific articles, data science competitions and of course my own experience as a data scientist. Specifically, you will learn: How to remove features with low variance How to identify redundant features How to select features based on statistical tests How to select features based on changes in model performance How to find predictive features based on importance attributed by models How to code procedures elegantly and in a professional manner How to leverage the power of existing Python libraries for feature selection Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, Scikit-learn, pandas and mlxtend. At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production. This comprehensive feature selection course includes about 70 lectures spanning ~8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects. In addition, I update the course regularly, to keep up with the Python libraries new releases and include new techniques when they appear. So what are you waiting for? Enroll today, embrace the power of feature selection and build simpler, faster and more reliable machine learning models. | https://www.udemy.com/course/feature-selection-for-machine-learning/#instructor-1 | Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science. As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations. Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning. Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics. Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions. Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities. Feel free to contact her on LinkedIn. ======================== Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos. Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones. A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina. Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos. Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones. Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades. No dudes en contactarla en LinkedIn. | Machine Learning | Chief/Lead Role | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
PyTorch for Deep Learning and Computer Vision | Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch | 4.3 | 1823 | 11448 | Created by Rayan Slim, Jad Slim, Amer Sharaf, Sarmad Tanveer | Sep-20 | English | $12.99 | 14h 14m total length | https://www.udemy.com/course/pytorch-for-deep-learning-and-computer-vision/ | Rayan Slim | Developer | 4.6 | 22052 | 165102 | PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch. Learn & Master Deep Learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course. You'll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen. By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch. The projects built in this course will impress even the most senior developers and ensure you have hands on skills that you can bring to any project or company. This course will show you to: Learn how to work with the tensor data structure Implement Machine and Deep Learning applications with PyTorch Build neural networks from scratch Build complex models through the applied theme of advanced imagery and Computer Vision Learn to solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models Use style transfer to build sophisticated AI applications that are able to seamlessly recompose images in the style of other images. No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers. This course also comes with all the source code and friendly support in the Q&A area. Who this course is for: Anyone with an interest in Deep Learning and Computer Vision Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence Entrepreneurs with an interest in working on some of the most cutting edge technologies All skill levels are welcome! | https://www.udemy.com/course/pytorch-for-deep-learning-and-computer-vision/#instructor-1 | Rayan is a full-stack software developer based in Ottawa, Canada. Rayan has been appointed as an acting tech lead at Canada's IRCC. His main role is to set up infrastructure monitoring tools to extract health metrics from cloud-native applications. Rayan also takes leadership roles as he guides other developers towards building Spring Boot applications that implement Enterprise Integration Patterns using the Apache Camel framework. His supervision extends to showing developers how to deploy their applications on the Red Hat Openshift platform using the Kubernetes package manager Helm. Outside of his daily work, Rayan loves to explore new technologies. He is deeply passionate about Artificial Intelligence and Data Visualization. In Rayan's free time, he loves to teach! | PyTorch | Engineer/Developer | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Docker Course for Beginners | Dive into the world of Docker and learn about Dockerfiles and Container Management | 4.1 | 1815 | 161453 | Created by EdYoda Digital University | Jun-20 | English | $9.99 | 1h 27m total length | https://www.udemy.com/course/docker-container-course-for-beginners/ | EdYoda Digital University | Visit us at www.edyoda.com | 4.2 | 37880 | 1018982 | Containerization of the applications is going on in the full swing across the IT industry. Docker's course covers the fundamental concepts of Docker containers. Along with the concepts it also covers the most useful commands related to container management, image management, and Dockerfile. After studying this course one would be ready to dive deeper into the world of container orchestration. Docker's course becomes the necessary prerequisite for learning Docker Swarm and Kubernetes. Enroll now!! see you in class. Happy Learning! Team Edyoda | https://www.udemy.com/course/docker-container-course-for-beginners/#instructor-1 | EdYoda is re-imagining skill based education, educating on job-relevant real world skills. Edyoda courses are on job-relevant technical skills. We have professional team of instructors, some of the courses we specialize in are Web development, Mobile App Development, Cloud & DevOps, Machine Learning, Artificial Intelligence and Big Data. We believe that access to education and opportunities is the biggest enabler and we are on a mission to enable the same for everyone across the world. | Misc | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Complete Data Science Training with Python for Data Analysis | Beginners python data analytics : Data science introduction : Learn data science : Python data analysis methods tutorial | 4.4 | 1754 | 9373 | Created by Minerva Singh | Oct-21 | English | $9.99 | 12h 56m total length | https://www.udemy.com/course/complete-data-science-training-with-python-for-data-analysis/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | Complete Guide to Practical Data Science with Python: Learn Statistics, Visualization, Machine Learning & More THIS IS A COMPLETE DATA SCIENCE TRAINING WITH PYTHON FOR DATA ANALYSIS: It's A Full 12-Hour Python Data Science BootCamp To Help You Learn Statistical Modelling, Data Visualization, Machine Learning & Basic Deep Learning In Python! HERE IS WHY YOU SHOULD TAKE THIS COURSE: First of all, this course a complete guide to practical data science using Python... That means, this course covers ALL the aspects of practical data science and if you take this course alone, you can do away with taking other courses or buying books on Python-based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By storing, filtering, managing, and manipulating data in Python, you can give your company a competitive edge & boost your career to the next level! THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE! But, first things first, My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals. Over the course of my research, I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning... This gives the student an incomplete knowledge of the subject. This course will give you a robust grounding in all aspects of data science, from statistical modelling to visualization to machine learning. Unlike other Python instructors, I dig deep into the statistical modelling features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out simple visualizations and data explorations to statistical analysis to machine learning to finally implementing simple deep learning-based models using Python DISCOVER 12 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON DATA SCIENCE (INCLUDING): • A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • A comprehensive presentation about basic analytical tools- Numpy Arrays, Operations, Arithmetic, Equation-solving, Matrices, Vectors, Broadcasting, etc. • Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. • Creating data visualizations like histograms, boxplots, scatterplots, bar plots, pie/line charts, and more! • Statistical analysis, statistical inference, and the relationships between variables • Machine Learning, Supervised Learning, Unsupervised Learning in Python • You’ll even discover how to create artificial neural networks and deep learning structures...& MUCH MORE! With this course, you’ll have the keys to the entire Python Data Science kingdom! NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable Python Data Science basics and techniques... I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real life. After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python. You’ll even understand deep concepts like statistical modelling in Python’s Statsmodels package and the difference between statistics and machine learning (including hands-on techniques). I will even introduce you to deep learning and neural networks using the powerful H2o framework! With this Powerful All-In-One Python Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and deep learning! The underlying motivation for the course is to ensure you can apply Python-based data science on real data and put into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with actual examples of your data science abilities. HERE IS WHAT THIS COURSE WILL DO FOR YOU: This course is your one shot way of acquiring the knowledge of statistical data analysis skills that I acquired from the rigorous training received at two of the best universities in the world, a perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. This course will: (a) Take students without a prior Python and/or statistics background from a basic level to performing some of the most common advanced data science techniques using the powerful Python-based Jupyter notebooks. (b) Equip students to use Python for performing different statistical data analysis and visualization tasks for data modelling. (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that students can apply these concepts for practical data analysis and interpretation. (d) Students will get a strong background in some of the most important data science techniques. (e) Students will be able to decide which data science techniques are best suited to answer their research questions and applicable to their data and interpret the results. It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, the majority of the course will focus on implementing different techniques on real data and interpret the results. After each video, you will learn a new concept or technique which you may apply to your own projects. JOIN THE COURSE NOW! #data #analysis #python #anaconda #analytics | https://www.udemy.com/course/complete-data-science-training-with-python-for-data-analysis/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Python | Data Scientist | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Complete & Practical SAS, Statistics & Data Analysis Course | A complete guide and use cases study for job seekers and beginners -- start career in SAS, Statistics and Data science | 4.5 | 1682 | 10565 | Created by Shenggang Li | Dec-18 | English | $9.99 | 16h 36m total length | https://www.udemy.com/course/complete-practical-sas-statistics-data-analysis-course/ | Shenggang Li | Senior Data Scientist | 4.2 | 1836 | 17263 | You should take this course! • If you need a complete and comprehensive package that covers SAS programming, intuitive statistics interpretation, data analysis, and predictive modeling, and • If you would like to learn by doing various practical use cases fitting in the positions in different business portfolios, and • Whether you are a job seeker or beginner intending to start a data science career Then this around 18 hours course is right for you! This complete SAS course includes more than 150 lectures and contains 11 real world case studies/projects in different applied areas such as banking and marketing. After this intensive training, you will be equipped with a powerful tool for the most sexy data analytics career path! | https://www.udemy.com/course/complete-practical-sas-statistics-data-analysis-course/#instructor-1 | Having successfully led the development of cutting-edge risk models using Big data at multiple major financial institutions and excelled in the advanced analytics field for the past 15 years, I am very enthusiastic at transferring knowledge and skills to the job seekers and new comers in the field of data analytics and application to business. I hold a PhD in Statistics and operational research. I am also a passionate educator, teaching as a principal instructor of a Toronto-based college, including advanced SAS data mining, Python & R for data science and machine learning and Big data analytics foundation and projects and for over 10 years. As a result, I have helped many of my students land their dream jobs in advanced analytics. I am also a Big data experts in machine learning, predictive modelling and retail/marketing analytics in Canada. My work includes but not limited to: implementation of Big data analysis for credit bureau, model vetting and validation for banking capital market, and customers attrition and life stage/life style segmentation for retail banks as well as big market firms. I also worked closely with senior executives and Big data architects in the field of health science to provide strategic advice. | Statistics | Data Scientist | Yes | >=4 | Below 10K | >=10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Practical Deep Learning with PyTorch | Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework. | 4.2 | 1676 | 6598 | Created by Deep Learning Wizard | Oct-18 | English | $10.99 | 6h 26m total length | https://www.udemy.com/course/practical-deep-learning-with-pytorch/ | Deep Learning Wizard | Deep Learning Researcher, NUS | 4.2 | 1676 | 6598 | Growing Importance of Deep Learning Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. Code As You Learn This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax. Gradual Learning Style The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start. Diagram-Driven Code This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefully create such that you can clearly see the transition from one model to another and understand the models comprehensively. Also, the diagrams are created so you can clearly see the link between the theory that I would teach and the code you would learn. Mentor Availability When I first started learning, I wished I had a mentor to guide me through the basics till the advanced theories where you can publish research papers and/or implement very complicated projects. And this course provides you with free access to ask any question, no matter how basic. I will be there and try my very best to answer your question. Even if the material is covered here, I will take the effort to point you to where you can learn here and more resources beyond this course. Math Prerequisite FAQ This is not a course that emphasizes heavily on the mathematics behind deep learning. It focuses on getting you to understand how everything works first which is very important for you to easily catch up on the mathematics later on. There are mathematics involved but they are limited with the sole aim to enhance your understanding and provide a gentle learning curve for future courses that would dive much deeper into it. Latest Python Notebooks Compatible with PyTorch 0.4 and 1.0 There are very small changes from PyTorch 0.3 for this deep learning series where you will find it is extremely easy to transit over! | https://www.udemy.com/course/practical-deep-learning-with-pytorch/#instructor-1 | Currently I am leading artificial intelligence with my colleagues in ensemblecap, an AI hedge fund based in Singapore comprising quants and traders from JPMorgan and Nomura. I have built the whole AI tech stack in a production environment with rigorous time-sensitive and fail-safe software testing powering multi-million dollar trades daily. I am also an NVIDIA Deep Learning Institute instructor leading all deep learning workshops in NUS, Singapore and conducting workshops across Southeast Asia. My passion for enabling anyone to leverage on deep learning has led to the creation of Deep Learning Wizard where I have taught and still continue to teach more than 2000 students in over 60 countries around the world. The course is recognized by Soumith Chintala, Facebook AI Research, and Alfredo Canziani, Post-Doctoral Associate under Yann Lecun, as the first comprehensive PyTorch Video Tutorial. In my free time, I’m into deep learning research with researchers based in NExT++ (NUS) led by Chua Tat-Seng and MILA led by Yoshua Bengio. I was previously conducting research in meta-learning for hyperparameter optimization for deep learning algorithms in NExT Search Centre that is jointly setup between National University of Singapore (NUS), Tsinghua University and University of Southampton led by co-directors Prof Tat-Seng Chua (KITHCT Chair Professor at the School of Computing), Prof Sun Maosong (Dean of Department of Computer Science and Technology, Tsinghua University), and Prof Dame Wendy Hall (Director of the Web Science Institute, University of Southampton). During my time there, I managed to publish in top-tier conferences and workshops like ICML and IJCAI. | PyTorch | Researcher | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Learn Python for Data Science & Machine Learning from A-Z | Become a professional Data Scientist and learn how to use NumPy, Pandas, Machine Learning and more! | 4.4 | 1636 | 112432 | Created by Juan E. Galvan, Ahmed Wael | Oct-21 | English | $9.99 | 22h 54m total length | https://www.udemy.com/course/python-for-data-science-machine-learning/ | Juan E. Galvan | Digital Entrepreneur | Business Coach | 4.5 | 18233 | 513094 | Learn Python for Data Science & Machine Learning from A-Z In this practical, hands-on course you’ll learn how to program using Python for Data Science and Machine Learning. This includes data analysis, visualization, and how to make use of that data in a practical manner. Our main objective is to give you the education not just to understand the ins and outs of the Python programming language for Data Science and Machine Learning, but also to learn exactly how to become a professional Data Scientist with Python and land your first job. We'll go over some of the best and most important Python libraries for data science such as NumPy, Pandas, and Matplotlib + NumPy — A library that makes a variety of mathematical and statistical operations easier; it is also the basis for many features of the pandas library. Pandas — A Python library created specifically to facilitate working with data, this is the bread and butter of a lot of Python data science work. NumPy and Pandas are great for exploring and playing with data. Matplotlib is a data visualization library that makes graphs as you’d find in Excel or Google Sheets. Blending practical work with solid theoretical training, we take you from the basics of Python Programming for Data Science to mastery. This Machine Learning with Python course dives into the basics of machine learning using Python. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each. We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the Python programming language, this course is for you! Python coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding Python coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques. Together we’re going to give you the foundational education that you need to know not just on how to write code in Python, analyze and visualize data and utilize machine learning algorithms but also how to get paid for your newly developed programming skills. The course covers 5 main areas: 1: PYTHON FOR DS+ML COURSE INTRO This intro section gives you a full introduction to the Python for Data Science and Machine Learning course, data science industry, and marketplace, job opportunities and salaries, and the various data science job roles. Intro to Data Science + Machine Learning with Python Data Science Industry and Marketplace Data Science Job Opportunities How To Get a Data Science Job Machine Learning Concepts & Algorithms 2: PYTHON DATA ANALYSIS/VISUALIZATION This section gives you a full introduction to the Data Analysis and Data Visualization with Python with hands-on step by step training. Python Crash Course NumPy Data Analysis Pandas Data Analysis 3: MATHEMATICS FOR DATA SCIENCE This section gives you a full introduction to the mathematics for data science such as statistics and probability. Descriptive Statistics Measure of Variability Inferential Statistics Probability Hypothesis Testing 4: MACHINE LEARNING This section gives you a full introduction to Machine Learning including Supervised & Unsupervised ML with hands-on step-by-step training. Intro to Machine Learning Data Preprocessing Linear Regression Logistic Regression K-Nearest Neighbors Decision Trees Ensemble Learning Support Vector Machines K-Means Clustering PCA 5: STARTING A DATA SCIENCE CAREER This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training. Creating a Resume Creating a Cover Letter Personal Branding Freelancing + Freelance websites Importance of Having a Website Networking By the end of the course you’ll be a professional Data Scientist with Python and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up. | https://www.udemy.com/course/python-for-data-science-machine-learning/#instructor-1 | Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets. | Machine Learning | Founder/Entrepreneur | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Data Manipulation in Python: Master Python, Numpy & Pandas | Learn Python, NumPy & Pandas for Data Science: Master essential data manipulation for data science in python | 4.2 | 1610 | 117149 | Created by Meta Brains | Apr-22 | English | $9.99 | 3h 46m total length | https://www.udemy.com/course/master-data-science-in-python/ | Meta Brains | Let's code & build the metaverse together! | 4.2 | 7800 | 319977 | When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find. Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods. That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years. The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist. Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python's high learning curve. It's a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we've previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas. You'll walk away from each video with a fresh idea that you can put to use right away! All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed! | https://www.udemy.com/course/master-data-science-in-python/#instructor-1 | Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel. We bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together! | Python | >=4 | Below 10K | >=1 Lakh | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Data Manipulation in Python: A Pandas Crash Course | Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python. | 4.6 | 1606 | 37501 | Created by Samuel Hinton | Apr-21 | English | $11.99 | 8h 47m total length | https://www.udemy.com/course/data-manipulation-in-python/ | Samuel Hinton | Astrophysicist, Software Engineer and Presenter | 4.5 | 4146 | 88236 | In the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable. If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back. Own your data, don’t let your data own you! When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success. Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively. This course prepares you to do just that! With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas. If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you. Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton: Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible. Achieve better results by spending more time problem-solving and less time data-wrangling. Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible. Utilize the latest version of Python and the industry-standard Pandas library. Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on: 1. Pandas has a steep learning curve: As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas. 2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help. After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of data analysis. Here’s a closer look at the curriculum: Loading and creating Pandas DataFrames Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations. Performing basic DataFrame manipulations: indexing, labeling, ordering slicing, filtering and more. Performing advanced Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more. Carrying out DataFrame grouping: aggregation, imputation, and more. Mastering time series manipulations: reindexing, resampling, rolling functions, method chaining and filtering, and more. Merging Pandas DataFrames Lastly, this course is packed with a cheatsheet and practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too. | https://www.udemy.com/course/data-manipulation-in-python/#instructor-1 | Hi, I'm Sam and I'm an astrophysicist, data scientist, robotics and software engineer, astronomer and public presenter. My work right now is all about renewable energy. Battery assets, optimising their utilisation and trading energy in markets to cut out as many fossil fuel generators as humanly possible. In academia, my primary work involves investigating the nature of dark energy, however I also spend a lot of time advocating of open-source development and proper coding practices. With years of experience from the financial software industry to machine learning pipelines classifying objects in the night sky, and teaching experience in statistics, software engineering, data manipulation, computational physics, and much more, I'm dedicated to increasing the level of coding proficiency in the scientific fields, and bringing basic coding knowledge to any eager student. On top of my research work, I've run national coding workshops with content ranging from complete novices up to research experts. I'm excited to bring my knowledge and content to a wider audience, and hope that my direct and to-the-point teaching attitude allows students to understand the core concepts faster and better, saving students time and stress! | Python | Engineer/Developer | >=4 | Below 10K | >=35K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Cutting-Edge AI: Deep Reinforcement Learning in Python | Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG | 4.6 | 1581 | 26650 | Created by Lazy Programmer Inc. | Nov-22 | English | $13.99 | 8h 32m total length | https://www.udemy.com/course/cutting-edge-artificial-intelligence/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | Welcome to Cutting-Edge AI! This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course. Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks). While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer. Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be. We’ve seen how AlphaZero can master the game of Go using only self-play. This is just a few years after the original AlphaGo already beat a world champion in Go. We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation. Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done. We’ve seen real-world robots learn hand dexterity, which is no small feat. Walking is one thing, but that involves coarse movements. Hand dexterity is complex - you have many degrees of freedom and many of the forces involved are extremely subtle. Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult. Last but not least - video games. Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2. So what makes this course different from the first two? Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms? This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies. Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more "black box" approach, inspired by biological evolution. What’s also great about this new course is the variety of environments we get to look at. First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone. Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics - we first have to show it can work with simulated physics. Finally, we’re going to look at Flappy Bird, everyone’s favorite mobile game just a few years ago. Thanks for reading, and I’ll see you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested prerequisites: Calculus Probability Object-oriented programming Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations Linear regression Gradient descent Know how to build a convolutional neural network (CNN) in TensorFlow Markov Decision Proccesses (MDPs) WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/cutting-edge-artificial-intelligence/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Python | Engineer/Developer | >=4 | Below 10K | >=25K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
PyTorch: Deep Learning and Artificial Intelligence | Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More! | 4.7 | 1515 | 6589 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $79.99 | 24h 4m total length | https://www.udemy.com/course/pytorch-deep-learning/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Welcome to PyTorch: Deep Learning and Artificial Intelligence! Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence. Is it possible that Tensorflow is popular only because Google is popular and used effective marketing? Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems? It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. 😉 On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam. If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Current projects include: Natural Language Processing (NLP) Recommender Systems Transfer Learning for Computer Vision Generative Adversarial Networks (GANs) Deep Reinforcement Learning Stock Trading Bot Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches). I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this! In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here. Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics. Thanks for reading, and I’ll see you in class! WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/pytorch-deep-learning/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | PyTorch | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Applied Statistical Modeling for Data Analysis in R | Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R | Bestseller | 4.3 | 1515 | 10149 | Created by Minerva Singh | Oct-22 | English | $14.99 | 9h 49m total length | https://www.udemy.com/course/applied-statistical-modeling-for-data-analysis-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | APPLIED STATISTICAL MODELING FOR DATA ANALYSIS IN R COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R Confounded by Confidence Intervals? Pondering Over p-values? Hankering Over Hypothesis Testing? Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course! I created this course to take you by hand teach you all the concepts, and take your statistical modelling from basic to an advanced level for practical data analysis. With this course, I want to help you save time and learn what the arcane statistical concepts have to do with the actual analysis of data and the interpretation of the bespoke results. Frankly, this is the only course you need to complete in order to get a head start in practical statistical modelling for data analysis using R. My course has 9.5 hours of lectures and provides a robust foundation to carry out PRACTICAL, real-life statistical data analysis tasks in R, one of the most popular and FREE data analysis frameworks. GET ACCESS TO A COURSE THAT IS JAM-PACKED WITH TONS OF APPLICABLE INFORMATION! AND GET A FREE VIDEO COURSE IN MACHINE LEARNING AS WELL! This course is your sure-fire way of acquiring the knowledge and statistical data analysis skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in renowned international journals like PLOS One. To be more specific, here’s what the course will do for you: (a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common advanced statistical data analysis tasks in R. (b) It will equip you to use R for performing the different statistical data analysis and visualization tasks for data modelling. (c) It will Introduce some of the most important statistical concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will learn some of the most important statistical modelling concepts from probability distributions to hypothesis testing to regression modelling and multivariate analysis. (e) You will also be able to decide which statistical modelling techniques are best suited to answer your research questions and applicable to your data and interpret the results. The course will mostly focus on helping you implement different statistical analysis techniques on your data and interpret the results. After each video, you will learn a new concept or technique which you may apply to your own projects immediately! TAKE ACTION NOW 🙂 You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. | https://www.udemy.com/course/applied-statistical-modeling-for-data-analysis-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Misc | Data Scientist | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
Artificial Intelligence in Web Design + Live Class | Learn incredible tools and tech in this course that cover Artificial Intelligence (AI) powered Web Design concepts | 4 | 1513 | 192356 | Created by Srinidhi Ranganathan, First Look Digital Marketing Solutions | Sep-22 | English | $9.99 | 3h 50m total length | https://www.udemy.com/course/artificial-intelligence-website-creation-2018-no-coding/ | Srinidhi Ranganathan | Digital Marketing Consultant | 3.9 | 24186 | 907599 | Welcome to learn, implement and master the course titled "Artificial Intelligence in Web Design (Special Edition)". What is this 2022 Special Edition Website Design course all about? Website design is the art and science of building the look, feel, and how a website functions in a nutshell. This course is having clear, concise, and easy to use website design technologies and will ultimately lead to a better user experience for your custom audience or clients. There are many aspects of successful website design like HTML, colours, layouts, text size, graphics, and so much more. But, this course is a huge differentiator in the design field as it uses artificial intelligence-based website design state-of-the-art technologies that are covered nowhere in the world. If you've been wondering how to learn website design, you've come to the right place - after all. Why website design is needed for a successful online presence? Having a good website is the backbone of every business and to achieve this successful feat, typically we need website design developers, content marketers, SEO specialists, etc. in an organization. But, some website design developers charge a lot of money outside in the market and other freelancing marketplaces to design websites using content management platforms like WordPress CMS. In fact, tools like Bookmark in website design will help advance your web-design career, altogether. This course is both a beginner's website design course and an advanced course for web developers. Jobs in Website Design in 2022: Unveiled According to Glassdoor, you can expect an average salary of $64,468 in the United States for doing website design. As your experience grows in website design all the time, you can expect to see higher salary ranges in the upcoming future. For example, you can expect an average Junior Website Designer to make around $62k in the United States alone and Rs. 2-3 Lakhs in India. As a Front End Website Design Developer, you can expect to make over $90k abroad. Introduction to the website design course powered by Artificial Intelligence (AI) technology: In the beginning website, design developers and designers designed websites using HTML. Soon, the internet was formless and empty, darkness was over the surface of the deep web, and the Spirit of Code was hovering over the pinnacle of utmost ignorance. We’ve come a long way from that time. The internet is still a dark, dreadful place, but it’s much more stylish, sophisticated, and amazing now. Website Design has grown exponentially in scale and sophistication over the last few years, thanks to new Artificial Intelligence-based website creation tools that are dominating the digital marketing industry. The technology is still in its infancy stage, however - but machine learning is enabling artificial design intelligence (ADI) to understand creative rules and apply them independently and in an intuitive and more attractive way - for that matter. Artificial design technology will soon be advanced enough to automate a lot of web design work in the near future too. New thought leaders would emerge and new courses like this would serve to be game-changers in the artificial intelligence-powered digital marketing space. This game-changing course focuses on "Artificial Intelligence in Web Design" taught by Digital Marketing Legend "Srinidhi Ranganathan" will cover artificial intelligence tools in website, chatbot design, and analytics in website design which will help you to create a website in merely minutes in 2022. I will teach you to easily create websites in the fastest time possible using advanced website design tools or design techniques and customize your site look and feel according to your requirement in a simple drag-and-drop timeline by talking to chatbots. Why learn this artificial intelligence game-changing course on website design and how is this a differentiator? This website design course can change your life as a web developer or marketer. With no coding experience, you can create amazing looking websites and pave the path for unlimited designs and interchange content and play god using artificial intelligence tech. This course will save you a ton of time when it comes to creating websites without using any expensive website design tool and without using complex tools like WordPress etc. You do not even need to outsource websites to other agencies ever again as you can do it yourself now in minutes. About Bookmark - The 2022 Artificial Intelligence Based Website Design Builder: The World-Changer Bookmark is an AI-powered website builder to help you design amazing websites at lightning speed. Bookmarks AI software AIDA (Artificial Intelligence Design Assistant) - The algorithm behind the website design empowers the non-technical entrepreneur and small business owner with the ability to instantly create an exceptional website that one can be proud of. AIDA eliminates up to 90% of the pain points associated with website design and creation by building a brand new, striking website in less than 30 seconds and then simply walks the user through the process of editing content and design. AIDA's features taught in the course comprises automatically moving the mouse cursor to aid in the website design process and instant change of website design style and fonts in a matter of minutes. The question is "Are you ready to get into action and embrace the power to leverage artificial intelligence in website design using AIDA?”. If yes, plunge into action right away by signing up NOW. All the best to become an Artificial Intelligence Web-Design Creator. IMPORTANT: Regarding the Live Group Q&A Session: Students who have completed every lesson in the course will also get access to a free live online group Q&A session of 1 hour with Digital Marketing Legend "Srinidhi Ranganathan". You can clarify all your doubts there. Srinidhi will also showcase your career paths in AI-powered web design, and provide you with a full walkthrough of what you learned along with tips on how to improvise or implement the stuff taught. | https://www.udemy.com/course/artificial-intelligence-website-creation-2018-no-coding/#instructor-1 | Important Note: Feel free to connect with Srinidhi on LinkedIn anytime. Catch some secretive educational videos created by Srinidhi on YouTube to further help in your learning by clicking the button on the right. About Srinidhi Ranganathan: Digital Marketing Consultant and Marketing Legend "Srinidhi Ranganathan" is the Chief Executive Officer (CEO) and Managing Director of First Look Digital Marketing Solutions (India's First Artificial Intelligence Powered Digital Marketing company) located in Bangalore and is one of the top instructors in India who is teaching highly futuristic digital marketing-related courses on Udemy. He is a Technologist, Digital Marketing Coach, Author, and Video Creation Specialist with over 10+ years of AIDM experience and has worked at top companies in India. Using his innovative marketing expertise, Srinidhi provides consulting services to startups and established brands utilising strategic planning and an extensive marketing audit powered by AI. He deploys the most comprehensive digital marketing strategy to clients worldwide that takes into account KPIs, methodology, and research statistics (utilising competitive intelligence software). Creating a growth hacking plan, content strategy, marketing mix, target segmentation analysis, competitor case-studies, brand strategy, local and global market research are also part of the consulting process to speed up a typical company's growth. Digital Marketing Legend "Srinidhi Ranganathan" has also helped startups and companies to leverage the best digital marketing strategies powered by automation to multi-fold their revenues. Having over 900,000+ students on Udemy - he has facilitated digital marketing analysis and provided state-of-the-art marketing strategy ideas and tactical execution plans for top marketing companies in India including startups, SMB's and MNC's. This includes strategic brainstorming sessions, Artificial Intelligence-powered market analysis, market research related to digital performance, support of various AIDM marketing initiatives for new product and consumer promotional launches, etc. He uses real-time forecasting, predictive modelling, machine learning, advanced machine learning-based optimisation techniques for business, marketing, Artificial Intelligence (AI) driven customer engagement strategies, competition monitoring software and other world-class tools. Srinidhi gained popularity through the unique, practical yet engaging training methodologies he utilises to teach during the training sessions. Some of his training methods include gamified learning experiences conducted by virtual writing and teaching robots like "Aera 2.0" that prompt behavioural changes in students and bring forth a new kind of fascination among the crowd. These robots are virtual humans having super-intelligence capabilities. They can autonomously train anyone on topics ranging from ABC to Rocket Science, without human intervention. Srinidhi's passionate fans call him a "Digital Marketing Legend" and he's busy working on creating new virtual and humanoid robots to revolutionise education in India and the world in 2022. He is deemed to be an innovator in the field of Artificial Intelligence (AI) based Digital Marketing and is someone who has embraced many ideas and has created various environments in which team members are taught the required AI automation tools and resources to challenge the status quo, push boundaries and achieve super-extensive growth. His courses are a testament to where the future is actually heading. "My goal has always been to give my students the AI tools to be able to leverage their digital marketing experiences, tools that allow them to build marketing success, from a whole new innovative perspective." — Srinidhi Ranganathan Srinidhi is currently working on these ultra-futuristic advancements in 2022 and creating research papers that contain information that delves 100-300 years into the future: 1) Autonomous Self-Thinking Scientific Computers 2) Personal Teleportation through Photons 3) Rise of Thought-to-Text and Dream Recognition Machines 4) Memory Regeneration with Nanobots 5) Birth of Hyper-Reality 6) Virtual Robo-Babies 7) Space-Tourism for the Wealthy 8) Mini-Flying Cities, Creation and Expansion using Maglev Technology 9) Self-Driving Flying Cars 10) Holographic Pets 11) Space-Probes (Stellar Light-Travelling Machines) 12) Virtual Digital Humans (Mind-Clones) 13) Computer-Like Lifeforms 14) Decentralised Artificial Intelligence (AI) Technologies 15) Mind-Revealing Technology 16) Cyborgs at the Workplace 17) Invisible Food 18) Temperature-Adaptable Smart Wearable Clothing 19) Holodeck-style Underwater Starship Homes 20) Yottabyte Storage Tech for Unimaginable Cloud Data Storage 21) Atmospheric Water Generation Secrets (2022-2050) | Artificial Intelligence | Consultant | >=4 | Below 10K | >=1 Lakh | >=3 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Machine Learning: Natural Language Processing in Python (V2) | NLP: Use Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python | 4.7 | 1511 | 7164 | Created by Lazy Programmer Inc., Lazy Programmer Team | Nov-22 | English | $9.99 | 22h 11m total length | https://www.udemy.com/course/natural-language-processing-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | Hello friends! Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This is a massive 4-in-1 course covering: 1) Vector models and text preprocessing methods 2) Probability models and Markov models 3) Machine learning methods 4) Deep learning and neural network methods In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF-IDF, and you'll learn the basics of neural embedding methods like word2vec, and GloVe. You'll then apply what you learned for various tasks, such as: Text classification Document retrieval / search engine Text summarization Along the way, you'll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization. You'll be introduced briefly to classic NLP tasks such as parts-of-speech tagging. In part 2, which covers probability models and Markov models, you'll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning. In this course, you'll see how such probability models can be used in various ways, such as: Building a text classifier Article spinning Text generation (generating poetry) Importantly, these methods are an essential prerequisite for understanding how the latest Transformer (attention) models such as BERT and GPT-3 work. Specifically, we'll learn about 2 important tasks which correspond with the pre-training objectives for BERT and GPT. In part 3, which covers machine learning methods, you'll learn about more of the classic NLP tasks, such as: Spam detection Sentiment analysis Latent semantic analysis (also known as latent semantic indexing) Topic modeling This section will be application-focused rather than theory-focused, meaning that instead of spending most of our effort learning about the details of various ML algorithms, you'll be focusing on how they can be applied to the above tasks. Of course, you'll still need to learn something about those algorithms in order to understand what's going on. The following algorithms will be used: Naive Bayes Logistic Regression Principal Components Analysis (PCA) / Singular Value Decomposition (SVD) Latent Dirichlet Allocation (LDA) These are not just "any" machine learning / artificial intelligence algorithms but rather, ones that have been staples in NLP and are thus an essential part of any NLP course. In part 4, which covers deep learning methods, you'll learn about modern neural network architectures that can be applied to solve NLP tasks. Thanks to their great power and flexibility, neural networks can be used to solve any of the aforementioned tasks in the course. You'll learn about: Feedforward Artificial Neural Networks (ANNs) Embeddings Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) The study of RNNs will involve modern architectures such as the LSTM and GRU which have been widely used by Google, Amazon, Apple, Facebook, etc. for difficult tasks such as language translation, speech recognition, and text-to-speech. Obviously, as the latest Transformers (such as BERT and GPT-3) are examples of deep neural networks, this part of the course is an essential prerequisite for understanding Transformers. UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out Thank you for reading and I hope to see you soon! | https://www.udemy.com/course/natural-language-processing-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Artificial Intelligence Masterclass | Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models | 4.5 | 1489 | 14391 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $13.99 | 11h 59m total length | https://www.udemy.com/course/artificial-intelligence-masterclass/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1624105 | Today, we are bringing you the king of our AI courses...: The Artificial Intelligence MASTERCLASS Are you keen on Artificial Intelligence? Do want to learn to build the most powerful AI model developed so far and even play against it? Sounds tempting right... Then Artificial Intelligence Masterclass course is the right choice for you. This ultimate AI toolbox is all you need to nail it down with ease. You will get 10 hours step by step guide and the full roadmap which will help you build your own Hybrid AI Model from scratch. In this course, we will teach you how to develop the most powerful Artificial intelligence model based on the most robust Hybrid Intelligent System. So far this model proves to be the best state of the art AI ever created beating its predecessors at all the AI competitions with incredibly high scores. This Hybrid Model is aptly named the Full World Model, and it combines all the state of the art models of the different AI branches, including Deep Learning, Deep Reinforcement Learning, Policy Gradient, and even, Deep NeuroEvolution. By enrolling in this course you will have the opportunity to learn how to combine the below models in order to achieve best performing artificial intelligence system: Fully-Connected Neural Networks Convolutional Neural Networks Recurrent Neural Networks Variational AutoEncoders Mixed Density Networks Genetic Algorithms Evolution Strategies Covariance Matrix Adaptation Evolution Strategy (CMA-ES) Parameter-Exploring Policy Gradients Plus many others Therefore, you are not getting just another simple artificial intelligence course but all in one package combining a course and a master toolkit, of the most powerful AI models. You will be able to download this toolkit and use it to build hybrid intelligent systems. Hybrid Models are becoming the winners in the AI race, so you must learn how to handle them already. In addition to all this, we will also give you the full implementations in the two AI frameworks: TensorFlow and Keras. So anytime you want to build an AI for a specific application, you can just grab those model you need in the toolkit, and reuse them for different projects! Don’t wait to join us on this EPIC journey in mastering the future of the AI - the hybrid AI Models. | https://www.udemy.com/course/artificial-intelligence-masterclass/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Artificial Intelligence | Founder/Entrepreneur | >=4 | Below 10K | >=10K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
Modern Natural Language Processing in Python | Solve Seq2Seq and Classification NLP tasks with Transformer and CNN using Tensorflow 2 in Google Colab | 4.1 | 1480 | 47472 | Created by Martin Jocqueviel, Ligency I Team, Ligency Team | Nov-21 | English | $13.99 | 5h 46m total length | https://www.udemy.com/course/modern-nlp/ | Martin Jocqueviel | Freelance data scientist | 4.2 | 3100 | 55396 | Modern Natural Language Processing course is designed for anyone who wants to grow or start a new career and gain a strong background in NLP. Nowadays, the industry is becoming more and more in need of NLP solutions. Chatbots and online automation, language modeling, event extraction, fraud detection on huge contracts are only a few examples of what is demanded today. Learning NLP is key to bring real solutions to the present and future needs. Throughout this course, we will leverage the huge amount of speech and text data available online, and we will explore the main 3 and most powerful NLP applications, that will give you the power to successfully approach any real-world challenge. First, we will dive into CNNs to create a sentimental analysis application. Then we will go for Transformers, replacing RNNs, to create a language translation system. The course is user-friendly and efficient: Modern NL leverages the latest technologies—Tensorflow 2.0 and Google Colab—assuring you that you won’t have any local machine/software version/compatibility issues and that you are using the most up-to-date tools. | https://www.udemy.com/course/modern-nlp/#instructor-1 | After graduating in Physics and Mathematics from École Polytechnique in France, I specialized in Machine Learning and Artificial Intelligence at ENS. As a Mathematician I like to grasp the full implications behind every algorithm, while as a physicist I want to consider the reality of data from a practical point of view when building an AI. I decided to combined those two aspects of science to build inspiring, intuitive and useful courses for everyone! | NLP | Data Scientist | >=4 | Below 10K | >=45K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Hands On Natural Language Processing (NLP) using Python | Learn Natural Language Processing ( NLP ) & Text Mining by creating text classifier, article summarizer, and many more. | 4.8 | 1461 | 8823 | Created by Next Edge Coding | Sep-19 | English | $9.99 | 10h 33m total length | https://www.udemy.com/course/hands-on-natural-language-processing-using-python/ | Next Edge Coding | Full Stack Developer & Data Enthusiast | 4.3 | 2506 | 21553 | In this course you will learn the various concepts of natural language processing by implementing them hands on in python programming language. This course is completely project based and from the start of the course the main objective would be to learn all the concepts required to finish the different projects. You will be building a text classifier which you will use to predict sentiments of tweets in real time and you will also be building an article summarizer which will fetch articles from websites and find the summary. Apart from these you will also be doing a lot of mini projects through out the course. So, at the end of the course you will have a deep understanding of NLP and how it is applied in real world. | https://www.udemy.com/course/hands-on-natural-language-processing-using-python/#instructor-1 | I am a developer, gadget geek and data enthusiast. I am currently doing my Bachelors in Computer Science in India. I have been programming since the age of 12 and it's the thing I enjoy doing most. I have experience of working on several real life projects based on Machine Learning, Natural Language Processing, Big Data with Hadoop and Spark, JavaScript, Front and back end web development and may more. Currently I have 7000+ happy students on Udemy and still counting. Teaching is not my profession but passion. Whenever I learn something, I just love to share it with other people and that interest has brought me to this diverse platform. By the way, thank you for reading all that. Peace. | NLP | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Apache Spark 3 - Databricks Certified Associate Developer | Learn Apache Spark 3 With Scala & Earn the Databricks Associate Certification to prove your skills as data professional | Bestseller | 4.4 | 1449 | 8538 | Created by Wadson Guimatsa | May-22 | English | $9.99 | 4h 32m total length | https://www.udemy.com/course/apache-spark-3-databricks-certified-associate-developer/ | Wadson Guimatsa | Data Engineer | 4.4 | 3066 | 26305 | Do you want to learn how to handle massive amounts of data at scale? Learn Apache Spark 3 and pass the Databricks Certified Associate Developer for Apache Spark 3.0 Hi, My name is Wadson, and I’m a Databricks Certified Associate Developer for Apache Spark 3.0 In today’s data-driven world, Apache Spark has become the standard big-data cluster processing framework. Apache Spark is used for Data Engineering, Data Science, and Machine Learning. I will teach you everything you need to know about getting started with Apache Spark. You will learn the Architecture of Apache Spark and use it’s Core APIs to manipulate complex data. You will write queries to perform transformations such as Join, Union, GroupBy, and more. This course is for beginners. You do not need previous knowledge of Apache Spark. There are Notebooks available to download so that you can follow along with me in the videos. The Notebooks contains all the source code I use in the course. There are also Quizzes to help you assess your understanding of the topics. | https://www.udemy.com/course/apache-spark-3-databricks-certified-associate-developer/#instructor-1 | I'm a software developer specialized in building data-intensive applications. I've been developing software for over 10 years. I've worked for Industries that are very data-intensive such as the financials and industrial image processing. Over the years, the volume of data produced by systems and humans outgrew the storage and compute capacity of the legacy RDBMS systems, and therefore I had to learn how to use the new tools and frameworks to process Big-Data As a data engineer, I'm very motivated and passionate about building applications that can leverage the power and flexibility of cloud computing and big-data processing frameworks. | Spark | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Data Integration Guide | Learn how data can be integrated : following which principles and for what business outcomes | 4.4 | 1444 | 5258 | Created by Ahmed Fessi | Nov-22 | English | $9.99 | 2h 16m total length | https://www.udemy.com/course/data-integration-guide/ | Ahmed Fessi | FinTech CIO | Data Leader | Enterprise Architect | Author | 4.4 | 1444 | 5258 | According to the World Economic Forum, at the beginning of 2020, the number of bytes in the digital universe was 40 times bigger than the number of stars in the observable universe. With data volume and usages growing, the need for Data Integration is becoming more and more central topic. Data Integration is mainly about exchanging data across multiple systems and tools. Aligned with their business strategy, organizations need data to circulate timely and accurately through their information system and the external world (internet applications, trading partners ..). This allows organizations to answer market needs, be competitive, reduce time to market, and become data driven by easing decision making processes. In this course, we are presenting a complete guide on how to identify your need of data integration, how you can architecture your solutions, execute successfully your projects and manage data integration overtime, all of this in order to bring tangible business value and to support your business. In more details we will address the following topics around Data Integration : What is Data Integration ? Data Integration Benefits & Business Value Main Concepts & Features Data Integration Paradigms & Patterns, including, ESB, Enterprise Service Bus ETL, Extract Transform Load EDI, Electronic Data Interchange API, Application Programming Interface Connectors for Data Integration With Databases With Files With WebServices: SOAP, REST With Enterprise Applications like SAP Security and technical architecture High availability Data Encryption Cloud Deployments Data Integration Projects Data Integration Run Operations Quick Overview of market solutions Proprietary vs OpenSource Solution components Licencing and pricing models Data Integration as Enabler for Digital Transformation This course is intended to be a complete practical guide to help you understand all the aspects around Data Integration. It can help you in your career and your current activities, by bringing a complete 360° overview on Data Integration topic. This course is intended to help : Chief Information Officers Chief Data Officers Chief Digital Officers Chief Analytics Officer Head of Data Head of Analytics IT Managers Business managers who work with Data Data Managers Enterprise Architects Data Project Managers Digital Projects Managers Data Analysts Data Specialists Data Engineers Data Scientists Data Architects Data Modelers IT Auditors Information System Performance Analysts And also, all students and professionals who want to benefit from the big market demand in Data and this important skill! No prior experience in Programming or Data Bases is needed to follow this course. This course is also vendor agnostic (and independent), whether you will work with solutions like Informatica, Talend, Boomi, OpenESB, Tibco ActiveMatrix, Mulesoft, IBM Websphere, Microsoft BizTalk or other, this course is generic enough to help you in your journey regardless of the solution you use or intend to use! It will even help you make the right choice based on your requirements and constraints. Throughout the course, you can easily contact the instructor for any questions you have to sharpen your knowledge and have tailored made learning experience! | https://www.udemy.com/course/data-integration-guide/#instructor-1 | FinTech CIO, Data Leader, Enterprise Architect & Author, passionate about Digital and Data Revolution, coming from engineering and computer science background, I spent good part of my career in charge of complex IT architectures definition and implementation. I had the opportunity to work on multiple industries and get broad view about Data & Digital Architectures overall. I am sharing critical views on Data approaches in Startups, SMBs and corporate world, with very practical and pragmatic views and advice. | Misc | Architect | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Databricks Fundamentals & Apache Spark Core | Learn how to process big-data using Databricks & Apache Spark 2.4 and 3.0.0 - DataFrame API and Spark SQL | Bestseller | 4.4 | 1416 | 18202 | Created by Wadson Guimatsa | Sep-21 | English | $11.99 | 12h 8m total length | https://www.udemy.com/course/databricks-fundamentals-apache-spark-core/ | Wadson Guimatsa | Data Engineer | 4.4 | 3066 | 26305 | Welcome to this course on Databricks and Apache Spark 2.4 and 3.0.0 Apache Spark is a Big Data Processing Framework that runs at scale. In this course, we will learn how to write Spark Applications using Scala and SQL. Databricks is a company founded by the creator of Apache Spark. Databricks offers a managed and optimized version of Apache Spark that runs in the cloud. The main focus of this course is to teach you how to use the DataFrame API & SQL to accomplish tasks such as: Write and run Apache Spark code using Databricks Read and Write Data from the Databricks File System - DBFS Explain how Apache Spark runs on a cluster with multiple Nodes Use the DataFrame API and SQL to perform data manipulation tasks such as Selecting, renaming and manipulating columns Filtering, dropping and aggregating rows Joining DataFrames Create UDFs and use them with DataFrame API or Spark SQL Writing DataFrames to external storage systems List and explain the element of Apache Spark execution hierarchy such as Jobs Stages Tasks | https://www.udemy.com/course/databricks-fundamentals-apache-spark-core/#instructor-1 | I'm a software developer specialized in building data-intensive applications. I've been developing software for over 10 years. I've worked for Industries that are very data-intensive such as the financials and industrial image processing. Over the years, the volume of data produced by systems and humans outgrew the storage and compute capacity of the legacy RDBMS systems, and therefore I had to learn how to use the new tools and frameworks to process Big-Data As a data engineer, I'm very motivated and passionate about building applications that can leverage the power and flexibility of cloud computing and big-data processing frameworks. | Spark | Engineer/Developer | >=4 | Below 10K | >=15K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Apache Cassandra in 2 hours | A complete guide for Cassandra architecture, Query language ,Cluster management, Java & Spark integration, | 4.2 | 1413 | 10066 | Created by Navdeep Kaur | Nov-21 | English | $10.99 | 2h 0m total length | https://www.udemy.com/course/learn-cassandra-from-scratch/ | Navdeep Kaur | Premium Instructor | TechnoAvengers.com (Founder) | 4.3 | 4118 | 51147 | This Apache Cassandra training course teaches you working with Cassandra. This course is intended for complete beginners in Cassandra. This is most concise, efficient and bestseller course on Apache Cassandra. In this course, we will what is Cassandra how to install Cassandra understand Cassandra data model with some hands on exercise which will teach you how to create a keyspace, create a table,insert and read the data . different data types in Cassandra with exercise. After this you will learn about the partition key and clustering key and understand how data is distributed across the nodes in a cluster.T I will covers the Cassandra Architecture in details in which we will cover replication, consistency, gossip protocol, write path, read path, Cassandra storage and compaction. Understanding anti patterns and data modeling goals. Understand Cassandra configuration files Working with nodetools to manage cluster Integrate with Cassandra java driver to write and run Cassandra from java program. Integrate Spark with Cassandra to perform analytics . Cassandra on AWS Once you have completed this video based training course, you will have a solid understanding of Cassandra, and be able to use Cassandra for your own development projects. Working files are included, allowing you to follow along with the author throughout the lessons. | https://www.udemy.com/course/learn-cassandra-from-scratch/#instructor-1 | Navdeep is one of the renowned Premium Instructor at Udemy. Navdeep has 12 years of industry experience in different technologies and domains. With 9+ courses and 40,000+ students and rating of 4.5*, she is one of the leading instructors in the field of Big Data & Cloud. Happy Learning! | Misc | Founder/Entrepreneur | >=4 | Below 10K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
The Complete Neural Networks Bootcamp: Theory, Applications | Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT! | 4.6 | 1409 | 11861 | Created by Fawaz Sammani | Nov-21 | English | $11.99 | 43h 47m total length | https://www.udemy.com/course/the-complete-neural-networks-bootcamp-theory-applications/ | Fawaz Sammani | Computer Vision Researcher | 4.5 | 2486 | 56652 | This course is a comprehensive guide to Deep Learning and Neural Networks. The theories are explained in depth and in a friendly manner. After that, we'll have the hands-on session, where we will be learning how to code Neural Networks in PyTorch, a very advanced and powerful deep learning framework! The course includes the following Sections: -------------------------------------------------------------------------------------------------------- Section 1 - How Neural Networks and Backpropagation Works In this section, you will deeply understand the theories of how neural networks and the backpropagation algorithm works, in a friendly manner. We will walk through an example and do the calculations step-by-step. We will also discuss the activation functions used in Neural Networks, with their advantages and disadvantages! Section 2 - Loss Functions In this section, we will introduce the famous loss functions that are used in Deep Learning and Neural Networks. We will walk through when to use them and how they work. Section 3 - Optimization In this section, we will discuss the optimization techniques used in Neural Networks, to reach the optimal Point, including Gradient Descent, Stochastic Gradient Descent, Momentum, RMSProp, Adam, AMSGrad, Weight Decay and Decoupling Weight Decay, LR Scheduler and others. Section 4 - Weight Initialization In this section,we will introduce you to the concepts of weight initialization in neural networks, and we will discuss some techniques of weights initialization including Xavier initialization and He norm initialization. Section 5 - Regularization Techniques In this section, we will introduce you to the regularization techniques in neural networks. We will first introduce overfitting and then introduce how to prevent overfitting by using regularization techniques, inclusing L1, L2 and Dropout. We'll also talk about normalization as well as batch normalization and Layer Normalization. Section 6- Introduction to PyTorch In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code! Section 7 - Practical Neural Networks in PyTorch - Application 1 In this section, you will apply what you've learned to build a Feed Forward Neural Network to classify handwritten digits. This is the first application of Feed Forward Networks we will be showing. Section 8 - Practical Neural Networks in PyTorch - Application 2 In this section, we will build a feed forward Neural Network to classify weather a person has diabetes or not. We will train the network on a large dataset of diabetes! Section 9 - Visualize the Learning Process In this section, we will visualize how neural networks are learning, and how good they are at separating non-linear data! Section 10 - Implementing a Neural Network from Scratch with Python and Numpy In this section, we will understand and code up a neural network without using any deep learning library (from scratch using only python and numpy). This is necessary to understand how the underlying structure works. Section 11 - Convolutional Neural Networks In this section, we will introduce you to Convolutional Networks that are used for images. We will show you first the relationship to Feed Forward Networks, and then we will introduce you the concepts of Convolutional Networks one by one! Section 12 - Practical Convolutional Networks in PyTorch In this section, we will apply Convolutional Networks to classify handwritten digits. This is the first application of CNNs we will do. Section 13- Deeper into CNN: Improving and Plotting In this section, we will improve the CNN that we built in the previous section, as well show you how to plot the results of training and testing! Moreover, we will show you how to classify your own handwritten images through the network! Section 14 - CNN Architectures In this section, we will introduce the CNN architectures that are widely used in all deep learning applications. These architectures are: AlexNet, VGG net, Inception Net, Residual Networks and Densely Connected Networks. We will also discuss some object detection architectures. Section 15- Residual Networks In this section, we will dive deep into the details and theory of Residual Networks, and then we'll build a Residual Network in PyTorch from scratch! Section 16 - Transfer Learning in PyTorch - Image Classification In this section, we will apply transfer learning on a Residual Network, to classify ants and bees. We will also show you how to use your own dataset and apply image augmentation. After completing this section, you will be able to classify any images you want! Section 17- Convolutional Networks Visualization In this section, we will visualize what the neural networks output, and what they are really learning. We will observe the feature maps of the network of every layer! Section 18 - YOLO Object Detection (Theory) In this section, we will learn one of the most famous Object Detection Frameworks: YOLO!! This section covers the theory of YOLO in depth. Section 19 - Autoencoders and Variational Autoencoders In this section, we will cover Autoencoders and Denoising Autoencoders. We will then see the problem they face and learn how to mitigate it with Variational Autoencoders. Section 20 - Recurrent Neural Networks In this section, we will introduce you to Recurrent Neural Networks and all their concepts. We will then discuss the Backpropagation through time, the vanishing gradient problem, and finally about Long Short Term Memory (LSTM) that solved the problems RNN suffered from. Section 21 - Word Embeddings In this section, we will discuss how words are represented as features. We will then show you some Word Embedding models. We will also show you how to implement word embedding in PyTorch! Section 22 - Practical Recurrent Networks in PyTorch In this section, we will apply Recurrent Neural Networks using LSTMs in PyTorch to generate text similar to the story of Alice in Wonderland! You can just replace the story with any other text you want, and the RNN will be able to generate text similar to it! Section 23 - Sequence Modelling In this section, we will learn about Sequence-to-Sequence Modelling. We will see how Seq2Seq models work and where they are applied. We'll also talk about Attention mechanisms and see how they work. Section 24 - Practical Sequence Modelling in PyTorch - Build a Chatbot In this section, we will apply what we learned about sequence modeling and build a Chatbot with Attention Mechanism. Section 25 - Saving and Loading Models In this section, we will show you how to save and load models in PyTorch, so you can use these models either for later testing, or for resuming training! Section 26 - Transformers In this section, we will cover the Transformer, which is the current state-of-art model for NLP and language modeling tasks. We will go through each component of a transformer. Section 27 - Build a Chatbot with Transformers In this section, we will implement all what we learned in the previous section to build a Chatbot using Transformers. | https://www.udemy.com/course/the-complete-neural-networks-bootcamp-theory-applications/#instructor-1 | I am a researcher doing my research in Computer Vision. Through out my research period, i have achieved many of my research goals and published multiple research papers. I have three courses, one which provides a complete guide to Image Processing with MATLAB, where you will master the basics of Image Processing and build interfaces for them, another course which is a complete guide to Neural Networks, where you'll master neural networks and deep learning topics in depth both theoretically and practically in one of the most powerful deep learning frameworks! I am extremely happy to share my knowledge and experience throughout my courses! | Neural Networks | Researcher | >=4 | Below 10K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Machine Learning Practical Workout | 8 Real-World Projects | Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks | 4.6 | 1386 | 14648 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Nov-22 | English | $9.99 | 14h 14m total length | https://www.udemy.com/course/deep-learning-machine-learning-practical/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | "Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology. Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled. Deep learning is widely used in self-driving cars, face and speech recognition, and healthcare applications. The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to: (1) Train Deep Learning techniques to perform image classification tasks. (2) Develop prediction models to forecast future events such as future commodity prices using state of the art Facebook Prophet Time series. (3) Develop Natural Language Processing Models to analyze customer reviews and identify spam/ham messages. (4) Develop recommender systems such as Amazon and Netflix movie recommender systems. The course is targeted towards students wanting to gain a fundamental understanding of Deep and machine learning models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real world challenging problems." | https://www.udemy.com/course/deep-learning-machine-learning-practical/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
AWS SageMaker Practical for Beginners | Build 6 Projects | Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML | 4.6 | 1386 | 8776 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Apr-21 | English | $11.99 | 16h 14m total length | https://www.udemy.com/course/practical-aws-sagemaker-6-real-world-case-studies/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | # Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot. # Update 23/04/2021 - Updated code scripts and addressed Q&A bugs. Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology. AWS is one of the most widely used ML cloud computing platforms worldwide – several Fortune 500 companies depend on AWS for their business operations. SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently. In this course, students will learn how to create AI/ML models using AWS SageMaker. Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning. The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way: Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization). AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML. Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent), machine learning training strategies (supervised/ unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, feature detectors, pooling, batch normalization, vanishing gradient problem, confusion matrix, precision, recall, F1-score, root mean squared error (RMSE), ensemble learning, decision trees, and random forest. We teach SageMaker’s vast range of ML and DL tools with practice-led projects. Delve into: Project #1: Train, test and deploy simple regression model to predict employees’ salary using AWS SageMaker Linear Learner Project #2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium. Project #3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker Hyperparameters tuning tool. Project #4: Perform Dimensionality reduction Using SageMaker built-in PCA algorithm and build a classifier model to predict cardiovascular disease using XGBoost Classification model. Project #5: Develop a traffic sign classifier model using Sagemaker and Tensorflow. Project #6: Deep Dive in AWS SageMaker Studio, AutoML, and model debugging. The course is targeted towards beginner developers and data scientists wanting to get fundamental understanding of AWS SageMaker and solve real world challenging problems. Basic knowledge of Machine Learning, python programming and AWS cloud is recommended. Here’s a list of who is this course for: Beginners Data Science wanting to advance their careers and build their portfolio. Seasoned consultants wanting to transform businesses by leveraging AI/ML using SageMaker. Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience using AWS SageMaker. Enroll today and I look forward to seeing you inside. | https://www.udemy.com/course/practical-aws-sagemaker-6-real-world-case-studies/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | AWS | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Deep Learning Foundation : Linear Regression and Statistics | Learn linear regression from scratch, Statistics, R-Squared, Python, Gradient descent, Deep Learning, Machine Learning | 3.3 | 1360 | 9198 | Created by Jay Bhatt | Feb-21 | English | $9.99 | 6h 31m total length | https://www.udemy.com/course/linear-regression-in-python-statistics-and-coding/ | Jay Bhatt | Data Scientist by Profession Instructor by Passion | 3.8 | 3446 | 28533 | Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test , Gradient descent. End of the course you will be able to code your own regression algorithm from scratch. | https://www.udemy.com/course/linear-regression-in-python-statistics-and-coding/#instructor-1 | Hi my name is Jay Having 5 years of experience in a leading Data Science Company, I have completed my masters degree adv mathematics and FEM . I love making educational videos and content. check out my you-tube channel and all udamy tutorial and stay updated with new techniques of data science and machine learning. Hope you will enjoy this lovely journey of Data science and machine learning. | Deep Learning | Data Scientist | Yes | >=3 | Below 10K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Complete Linear Regression Analysis in Python | Linear Regression in Python| Simple Regression, Multiple Regression, Ridge Regression, Lasso and subset selection also | 4.3 | 1333 | 155315 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 7h 28m total length | https://www.udemy.com/course/machine-learning-basics-building-regression-model-in-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right? You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear regression technique of Machine Learning. Create a linear regression model in Python and analyze its result. Confidently practice, discuss and understand Machine Learning concepts A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation Section 2 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data Preprocessing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 5 - Regression Model This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-basics-building-regression-model-in-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Python | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Artificial Intelligence in Digital Marketing + Live Class | Learn incredible Artificial Intelligence in Digital Marketing Automation Tools in 2022 | 4.2 | 1330 | 153337 | Created by Srinidhi Ranganathan, First Look Digital Marketing Solutions | Nov-22 | English | $9.99 | 10h 52m total length | https://www.udemy.com/course/artificial-intelligence-in-digital-marketing/ | Srinidhi Ranganathan | Digital Marketing Consultant | 3.9 | 24186 | 907599 | Welcome to experience "Artificial Intelligence in Digital Marketing - Gold Edition 2022." Artificial Intelligence has transformed the virtual panorama, inclusive of Google’s RankBrain personalising suggestions by Amazon. Artificial Intelligence (AI) is hastily turning into important in the daily happenings of the virtual global, with marketing and advertising and marketing being no exception. The idea of AI may also bring to thought 60’s sci-fi with futuristic robots, however, it’s definitely approximately so much greater. With the right understanding and evaluation of data and input, AI is playing an essential position in figuring out marketing trends. Brands and marketers are incorporating Machine Learning and Artificial Intelligence to save time and assets. Earlier, marketers have been reluctant to use AI in their marketing strategies. But now, many hit brands like Amazon and Spotify have adopted it and use it as their advertising gear. For example, Amazon makes use of AI to show the most effective relevant merchandise to buyers, based totally on preceding purchases, searches, and perspectives. It can likely boom the probabilities of a customer to purchase greater products with the pretty sought-after personalised enjoy. AI, as a part of virtual advertising, is now a fact, supplying an array of alternatives as well as benefits. Let’s now see how exactly artificial intelligence is making the future of digital advertising. Online looking content has also transformed. There have been two tremendous AI advances: search engine optimisation and revolutionised net searches. The voice seeks and Google’s set of new rules and RankBrain are the opposite AI improvements, as discussed earlier. Some different innovations which can be widely used nowadays encompass, Google Home, Amazon Echo, Apple’s Siri, and Microsoft’s Cortana make it convenient for human beings to carry out searches by simply urgent a button or pronouncing a voice command. AI-based CRM (customer relationship management) tools make it convenient to maximise the gathering of consumer details and facts from special technologies utilised and this process can be fully automated too. It additionally helps to gain unique insights into target audiences and identify customers’ needs so that groups can establish the most suitable advertising strategy. This behavioural data makes it easy for the brands to apprehend clients’ temper. For instance, the kinds of products someone buys, pages they browse, which gear they regularly use, etc. With all of these accrued facts, marketers can without problems examine a capacity client's needs, and adapt AI used to grow income. A note on Artificial Intelligence in Digital Marketing (2022 Gold Edition) - The Course This game-changing course taught in 2022 will cover artificial intelligence tools in content creation, curation, augmented reality, and digital marketing and will take you on a glimpse into the future. We will also look at influencer marketing tools, content trends and a bit of competitor analysis through the use of BuzzSumo. Why learn this amazing artificial intelligence course and how is this a differentiator for content creators? This course can change your life if you are a content expert. Because, I will provide you with hands-on experience in creating tons and tons of articles for your blog for inbound marketing using an Artificial Intelligence content tool and you don’t even have to write the content yourself - ever again. This will save you a lot of time and effort and you don’t have to employ a content team ever again. Artificial Intelligence is the key to unlocking your success path. Also, we will look at content curation tools to help you create a mark in this competitive brandscape. In augmented reality, we will show you how to use Zappar, the popular tool, and create a great experience with zap codes and how they come to life while you scan them with your mobile phone. How Artificial Intelligence-based Digital Marketing Revolution is on the rise? We are right at the beginning of the AI revolution but we already have a good sense of how artificial intelligence is going to change digital marketing's face. Artificial Intelligence usage in digital marketing will change the entire world with marketers embracing this futuristic tech. If you're not yet absolutely on the bandwagon or just start plunging your toes into the ice, you're not the only one. The way consumers respond to marketing messages and interact with them is changing. Traditional methods of marketing such as media advertising and direct mail are no longer as successful as they were once. One reason for this is that today's consumers expect marketers to adapt their demographics or preferences to their messages. Artificial Intelligence is starting to go hand in hand with digital marketing. AI transforms digital strategy with the ability to collect data, interpret it, apply it, and then learn from it. The ability to use it to improve digital marketing strategies and valuable customer insights for companies will be the same as it continues to advance. Excellent customer service is the also most important aspect of an effective digital marketing campaign this too can be automated by Google Duplex kind of tools that are created in the market. Why is this secretive course in "Artificial Intelligence in Digital Marketing - Gold Edition" - the best one in the industry? Artificial intelligence keeps growing and improving and will not slow down for a while. Implementing AI into your digital marketing strategy will help customers' experience better and provide your business with the insights it needs to succeed. What’s more? The ultimate secret of the course is that we will also tell you about a tool that you can use for unlimited lead-generation using another secretive artificial intelligence (AI) technology. There is often a disconnection between sales and marketing departments in many businesses. This is typically due to a lack of interaction or shared attention. Marketers may give sales a lead they believe to be ideal, but sales are not. It creates tension between two teams to work together, and AI is the perfect way to bring them together. AI can point out positive sales leads, which will also satisfy marketing, and ensure that potential customers are targeted at the right platforms. You don't want the company that sells accounting software to be advertised by someone. All teams will need to come together and agree on an optimal customer profile, a profile that fits marketing and sales, to better use AI in the B2B lead generation. The profile can consist of multiple variables, such as sector, company level, and how many employees they have. The standards are almost infinite. The AI will use the profile to measure current and potential leads against a selection of pre-defined behavioural criteria. Without us providing all this, the AI will fumble around, essentially throwing darts at a board while blindfolded. The AI can predict future patterns for lead generation, possible spending, and what campaigns or services will draw their interest from this. Ok, having given all this information that focuses on "Artificial intelligence in digital marketing", the question is "Are you ready to get into action and embrace the power to leverage artificial intelligence in digital marketing?”. If yes, plunge into action right away by signing up NOW. Remember, using Artificial Intelligence is the only way you can growth-hack your way to success ahead of the rest of the crowd who are currently into using only traditional digital marketing methodologies and tools. Your successful Artificial Intelligence career is waiting. Don't miss the bus. This is your golden ticket to learning AI (Artificial Intelligence) in Digital Marketing, passionate ones. IMPORTANT: Regarding the Live Group Q&A Session: Students who have completed every lesson in the course will also get access to a free live online group Q&A session of 1 hour with Digital Marketing Legend "Srinidhi Ranganathan". You can clarify all your doubts there. Srinidhi will also showcase your career paths in AIDM, and provide you with a full walkthrough of what you learned along with tips on how to improvise or implement the stuff taught. | https://www.udemy.com/course/artificial-intelligence-in-digital-marketing/#instructor-1 | Important Note: Feel free to connect with Srinidhi on LinkedIn anytime. Catch some secretive educational videos created by Srinidhi on YouTube to further help in your learning by clicking the button on the right. About Srinidhi Ranganathan: Digital Marketing Consultant and Marketing Legend "Srinidhi Ranganathan" is the Chief Executive Officer (CEO) and Managing Director of First Look Digital Marketing Solutions (India's First Artificial Intelligence Powered Digital Marketing company) located in Bangalore and is one of the top instructors in India who is teaching highly futuristic digital marketing-related courses on Udemy. He is a Technologist, Digital Marketing Coach, Author, and Video Creation Specialist with over 10+ years of AIDM experience and has worked at top companies in India. Using his innovative marketing expertise, Srinidhi provides consulting services to startups and established brands utilising strategic planning and an extensive marketing audit powered by AI. He deploys the most comprehensive digital marketing strategy to clients worldwide that takes into account KPIs, methodology, and research statistics (utilising competitive intelligence software). Creating a growth hacking plan, content strategy, marketing mix, target segmentation analysis, competitor case-studies, brand strategy, local and global market research are also part of the consulting process to speed up a typical company's growth. Digital Marketing Legend "Srinidhi Ranganathan" has also helped startups and companies to leverage the best digital marketing strategies powered by automation to multi-fold their revenues. Having over 900,000+ students on Udemy - he has facilitated digital marketing analysis and provided state-of-the-art marketing strategy ideas and tactical execution plans for top marketing companies in India including startups, SMB's and MNC's. This includes strategic brainstorming sessions, Artificial Intelligence-powered market analysis, market research related to digital performance, support of various AIDM marketing initiatives for new product and consumer promotional launches, etc. He uses real-time forecasting, predictive modelling, machine learning, advanced machine learning-based optimisation techniques for business, marketing, Artificial Intelligence (AI) driven customer engagement strategies, competition monitoring software and other world-class tools. Srinidhi gained popularity through the unique, practical yet engaging training methodologies he utilises to teach during the training sessions. Some of his training methods include gamified learning experiences conducted by virtual writing and teaching robots like "Aera 2.0" that prompt behavioural changes in students and bring forth a new kind of fascination among the crowd. These robots are virtual humans having super-intelligence capabilities. They can autonomously train anyone on topics ranging from ABC to Rocket Science, without human intervention. Srinidhi's passionate fans call him a "Digital Marketing Legend" and he's busy working on creating new virtual and humanoid robots to revolutionise education in India and the world in 2022. He is deemed to be an innovator in the field of Artificial Intelligence (AI) based Digital Marketing and is someone who has embraced many ideas and has created various environments in which team members are taught the required AI automation tools and resources to challenge the status quo, push boundaries and achieve super-extensive growth. His courses are a testament to where the future is actually heading. "My goal has always been to give my students the AI tools to be able to leverage their digital marketing experiences, tools that allow them to build marketing success, from a whole new innovative perspective." — Srinidhi Ranganathan Srinidhi is currently working on these ultra-futuristic advancements in 2022 and creating research papers that contain information that delves 100-300 years into the future: 1) Autonomous Self-Thinking Scientific Computers 2) Personal Teleportation through Photons 3) Rise of Thought-to-Text and Dream Recognition Machines 4) Memory Regeneration with Nanobots 5) Birth of Hyper-Reality 6) Virtual Robo-Babies 7) Space-Tourism for the Wealthy 8) Mini-Flying Cities, Creation and Expansion using Maglev Technology 9) Self-Driving Flying Cars 10) Holographic Pets 11) Space-Probes (Stellar Light-Travelling Machines) 12) Virtual Digital Humans (Mind-Clones) 13) Computer-Like Lifeforms 14) Decentralised Artificial Intelligence (AI) Technologies 15) Mind-Revealing Technology 16) Cyborgs at the Workplace 17) Invisible Food 18) Temperature-Adaptable Smart Wearable Clothing 19) Holodeck-style Underwater Starship Homes 20) Yottabyte Storage Tech for Unimaginable Cloud Data Storage 21) Atmospheric Water Generation Secrets (2022-2050) | Artificial Intelligence | Consultant | >=4 | Below 10K | >=1 Lakh | >=3 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Machine Learning and Data Science Hands-on with Python and R | Machine Learning, Statistics, Python, AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian, BI and much more | 3.9 | 1328 | 47279 | Created by EDUCBA Bridging the Gap | Aug-20 | English | $11.99 | 72h 16m total length | https://www.udemy.com/course/machine-learning-masterclass/ | EDUCBA Bridging the Gap | Learn real world skills online | 4 | 4791 | 252395 | Learn from well designed, well-crafted study materials on Machine Learning ML, Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Face Detection, Business Intelligence BI, Regression, Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic, Numpy, Pandas, Metplotlit, Seaborn, Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection, Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm, Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis. Learn by doing. Full Lifetime Access. Get the skills to work with implementations and develop capabilities that you can use to deliver results in a machine learning project. This program will help you build the foundation for a solid career in Machine learning Tools. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This program is a comprehensive understanding of AI concepts and its application using Python and iPython. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition “can be viewed as two facets of the same field. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence. | https://www.udemy.com/course/machine-learning-masterclass/#instructor-1 | EDUCBA is a leading global provider of skill based education addressing the needs of 1,000,000+ members across 70+ Countries. Our unique step-by-step, online learning model along with amazing 5000+ courses and 500+ Learning Paths prepared by top-notch professionals from the Industry help participants achieve their goals successfully. All our training programs are Job oriented skill based programs demanded by the Industry. At EDUCBA, it is a matter of pride for us to make job oriented hands-on courses available to anyone, any time and anywhere. Therefore we ensure that you can enroll 24 hours a day, seven days a week, 365 days a year. Learn at a time and place, and pace that is of your choice. Plan your study to suit your convenience and schedule. | Machine Learning | >=3 | Below 10K | >=45K | >=4 | Below 10 K | >=2.5 Lakh | ||||||||||||||||||
Scraping and Data Mining for Beginners and Pros | Data mining visually or programmatically, learn everything you need in a fast pace 30 minute course | 4.1 | 1305 | 29272 | Created by Ala Shiban | Oct-22 | English | $9.99 | 1h 2m total length | https://www.udemy.com/course/scraping-and-data-mining-for-beginners-and-pros-y/ | Ala Shiban | Founder @ Klotho (Klo.dev) | 4.3 | 1807 | 56229 | For Busy People Only! Web scraping is a technique for gathering data or information on web pages. You could revisit your favorite web site every time it updates for new information, or you could write a web scraper to have it do it for you! Web crawling is usually the very first step of data research. Whether you are looking to obtain data from a website, track changes on the internet, or use a website API, web crawlers are a great way to get the data you need. Learn everything you need to know about converting web sit es into data. We'll focus on the 20% that gets the 80% job done. We'll cover data mining approaches for journalists, growth hackers, data scientists and anyone who's fascinated about seeing the big picture. Presented are Visual tools and Programmatic tools that you can get started with, making the course accessible for both beginners and more experienced developers. We'll show you how data is represented, navigated and accessed. We'll briefly talk about other mechanisms like API stores, data stores and official APIs and their pros/cons. If you're busy, and want to learn how to unlock the power of data in 30 minutes, check this course out. | https://www.udemy.com/course/scraping-and-data-mining-for-beginners-and-pros-y/#instructor-1 | Software Engineer turned Product Manager, I'm passionate about people-empowering technology. Throughout the years, I've been involved with robotics, image processing, computer vision, biomedical research, volunteering groups, machine learning and teaching. My courses on Udemy are braindumps of how I explore new tools and topics, distilled to the bare minimum time needed to capture the most important lessons to be learned. | Misc | Founder/Entrepreneur | >=4 | Below 10K | >=25K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Time Series Analysis and Forecasting using Python | Learn about time series analysis & forecasting models in Python |Time Data Visualization|AR|MA|ARIMA|Regression| ANN | 4.3 | 1302 | 144385 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 13h 18m total length | https://www.udemy.com/course/machine-learning-time-series-forecasting-in-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right? You've found the right Time Series Forecasting and Time Series Analysis course using Python Time Series techniques. This course teaches you everything you need to know about different time series forecasting and time series analysis models and how to implement these models in Python time series. After completing this course you will be able to: Implement time series forecasting and time series analysis models such as AutoRegression, Moving Average, ARIMA, SARIMA etc. Implement multivariate time series forecasting models based on Linear regression and Neural Networks. Confidently practice, discuss and understand different time series forecasting, time series analysis models and Python time series techniques used by organizations How will this course help you? A Verifiable Certificate of Completion is presented to all students who undertake this Time Series Forecasting course on time series analysis and Python time series applications. If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it. You will also learn time series forecasting models, time series analysis and Python time series techniques. Why should you choose this course? We believe in teaching by example. This course is no exception. Every Section’s primary focus is to teach you the concepts through how-to examples. Each section has the following components: Theoretical concepts and use cases of different forecasting models, time series forecasting and time series analysis Step-by-step instructions on implement time series forecasting models in Python Downloadable Code files containing data and solutions used in each lecture on time series forecasting, time series analysis and Python time series techniques Class notes and assignments to revise and practice the concepts on time series forecasting, time series analysis and Python time series techniques The practical classes where we create the model for each of these strategies is something which differentiates this course from any other available online course on time series forecasting, time series analysis and Python time series techniques. .What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Analytics and we have used our experience to include the practical aspects of Marketing and data analytics in this course. They also have an in-depth knowledge on time series forecasting, time series analysis and Python time series techniques. We are also the creators of some of the most popular online courses - with over 170,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts on time series forecasting, time series analysis and Python time series techniques. Each section contains a practice assignment for you to practically implement your learning on time series forecasting, time series analysis and Python time series techniques. What is covered in this course? Understanding how future sales will change is one of the key information needed by manager to take data driven decisions. In this course, we will deal with time series forecasting, time series analysis and Python time series techniques. We will also explore how one can use forecasting models to See patterns in time series data Make forecasts based on models Let me give you a brief overview of the course Section 1 - Introduction In this section we will learn about the course structure and how the concepts on time series forecasting, time series analysis and Python time series techniques will be taught in this course. Section 2 - Python basics This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. The basics taught in this part will be fundamental in learning time series forecasting, time series analysis and Python time series techniques on later part of this course. Section 3 - Basics of Time Series Data In this section, we will discuss about the basics of time series data, application of time series forecasting, and the standard process followed to build a forecasting model, time series forecasting, time series analysis and Python time series techniques. Section 4 - Pre-processing Time Series Data In this section, you will learn how to visualize time series, perform feature engineering, do re-sampling of data, and various other tools to analyze and prepare the data for models and execute time series forecasting, time series analysis and implement Python time series techniques. Section 5 - Getting Data Ready for Regression Model In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation. Section 6 - Forecasting using Regression Model This section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results. Section 7 - Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Section 8 - Creating Regression and Classification ANN model in Python In this part you will learn how to create ANN models in Python. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models. I am pretty confident that the course will give you the necessary knowledge and skills related to time series forecasting, time series analysis and Python time series techniques to immediately see practical benefits in your work place. Go ahead and click the enroll button, and I'll see you in lesson 1 of this course on time series forecasting, time series analysis and Python time series techniques! Cheers Start-Tech Academy | https://www.udemy.com/course/machine-learning-time-series-forecasting-in-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Python | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Statistics / Data Analysis in SPSS: Inferential Statistics | Increase Your Data Analytic Skills – Highly Valued And Sought After By Employers | Bestseller | 4.7 | 1300 | 8283 | Created by Quantitative Specialists | Jun-15 | English | $11.99 | 4h 51m total length | https://www.udemy.com/course/inferential-statistics-spss/ | Quantitative Specialists | Specializing in Statistics, Research Design, and Measurement | 4.6 | 5017 | 23575 | November, 2019. Join more than 1,000 students and get instant access to this best-selling content - enroll today! Get marketable and highly sought after skills in this course that will substantially increase your knowledge of data analytics, with a focus in the area of significance testing, an important tool for A/B testing and product assessment. Many tests covered, including three different t tests, two ANOVAs, post hoc tests, chi-square tests (great for A/B testing), correlation, and regression. Database management also covered! Two in-depth examples provided of each test for additional practice. This course is great for professionals, as it provides step by step instruction of tests with clear and accurate explanations. Get ahead of the competition and make these tests important parts of your data analytic toolkit! Students will also have the tools needed to succeed in their statistics and experimental design courses. Data Analytics is an rapidly growing area in high demand (e.g., McKinsey) Statistics play a key role in the process of making sound business decisions that will generate higher profits. Without statistics, it's difficult to determine what your target audience wants and needs. Inferential statistics, in particular, help you understand a population's needs better so that you can provide attractive products and services. This course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line. Use Tests in SPSS to Correctly Analyze Inferential Statistics Use the One Sample t Test to Draw Conclusions about a Population Understand ANOVA and the Chi Square Master Correlation and Regression Learn Data Management Techniques Analyze Research Results Accurately to Make Better Business Decisions With SPSS, you can analyze data to make the right business decisions for your customer base. And by understanding how to use inferential statistics, you can draw accurate conclusions about a large group of people, based on research conducted on a sample of that population. This easy-to-follow course, which contains illustrative examples throughout, will show you how to use tests to assess if the results of your research are statistically significant. You'll be able to determine the appropriate statistical test to use for a particular data set, and you'll know how to understand, calculate, and interpret effect sizes and confidence intervals. You'll even know how to write the results of statistical analyses in APA format, one of the most popular and accepted formats for presenting the results of statistical analyses, which you can successfully adapt to other formats as needed. Contents and Overview This course begins with a brief introduction before diving right into the One Sample t Test, Independent Samples t Test, and Dependent Samples t Test. You'll use these tests to analyze differences and similarities between sample groups in a population. This will help you determine if you need to change your business plan for certain markets of consumers. Next, you'll tackle how to use ANOVA (Analysis of Variance), including Post-hoc Tests and Levene's Equal Variance Test. These tests will also help you determine what drives consumer decisions and behaviors between different groups. When ready, you'll master correlation and regression, as well as the chi-square. As with all previous sections, you'll see illustrations of how to analyze a statistical test, and you'll access additional examples for more practice. Finally, you'll learn about data management in SPSS, including sorting and adding variables. By the end of this course, you'll be substantially more confident in both IBM SPSS and statistics. You'll know how to use data to come to the right conclusions about your market. By understanding how to use inferential statistics, you'll be able to identify consumer needs and come up with products and/or services that will address those needs effectively. Join the over 1,000 students who have taken this best-selling course - enroll today! | https://www.udemy.com/course/inferential-statistics-spss/#instructor-1 | Quantitative Specialists (QS) was founded by an award-winning university instructor who has taught statistics courses for over 15 years. At QS, we are passionate about all things statistical, especially in helping others understand this often-feared subject matter. Our focus is in helping you to succeed in all your statistics work! | Statistics | Yes | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Learn Data Science & Machine Learning with R from A-Z | Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more! | 4.5 | 1293 | 94905 | Created by Juan E. Galvan, Ismail Tigrek | Jan-21 | English | $9.99 | 28h 39m total length | https://www.udemy.com/course/data-science-and-machine-learning-with-r-from-a-z/ | Juan E. Galvan | Digital Entrepreneur | Business Coach | 4.5 | 18233 | 513094 | Welcome to the Learn Data Science and Machine Learning with R from A-Z Course! In this practical, hands-on course you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. Our main objective is to give you the education not just to understand the ins and outs of the R programming language, but also to learn exactly how to become a professional Data Scientist with R and land your first job. The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery. We understand that theory is important to build a solid foundation, we understand that theory alone isn’t going to get the job done so that’s why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you! R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques. Together we’re going to give you the foundational education that you need to know not just on how to write code in R, analyze and visualize data but also how to get paid for your newly developed programming skills. The course covers 6 main areas: 1: DS + ML COURSE + R INTRO This intro section gives you a full introduction to the R programming language, data science industry and marketplace, job opportunities and salaries, and the various data science job roles. Intro to Data Science + Machine Learning Data Science Industry and Marketplace Data Science Job Opportunities R Introduction Getting Started with R 2: DATA TYPES/STRUCTURES IN R This section gives you a full introduction to the data types and structures in R with hands-on step by step training. Vectors Matrices Lists Data Frames Operators Loops Functions Databases + more! 3: DATA MANIPULATION IN R This section gives you a full introduction to the Data Manipulation in R with hands-on step by step training. Tidy Data Pipe Operator dplyr verbs: Filter, Select, Mutate, Arrange + more! String Manipulation Web Scraping 4: DATA VISUALIZATION IN R This section gives you a full introduction to the Data Visualization in R with hands-on step by step training. Aesthetics Mappings Single Variable Plots Two-Variable Plots Facets, Layering, and Coordinate System 5: MACHINE LEARNING This section gives you a full introduction to Machine Learning with hands-on step by step training. Intro to Machine Learning Data Preprocessing Linear Regression Logistic Regression Support Vector Machines K-Means Clustering Ensemble Learning Natural Language Processing Neural Nets 6: STARTING A DATA SCIENCE CAREER This section gives you a full introduction to starting a career as a Data Scientist with hands-on step by step training. Creating a Resume Personal Branding Freelancing + Freelance websites Importance of Having a Website Networking By the end of the course you’ll be a professional Data Scientist with R and confidently apply for jobs and feel good knowing that you have the skills and knowledge to back it up. | https://www.udemy.com/course/data-science-and-machine-learning-with-r-from-a-z/#instructor-1 | Hi I'm Juan. I've been an Entrepreneur since grade school. My background is in the tech space from Digital Marketing, E-commerce, Web Development to Programming. I believe in continuous education with the best of a University Degree without all the downsides of burdensome costs and inefficient methods. I look forward to helping you expand your skillsets. | Machine Learning | Founder/Entrepreneur | >=4 | Below 10K | >=50K | >=4 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Looker and LookML - The Complete Course for Beginners | Practical Looker course for beginners that want to quickly get up to speed with Looker and LookML | Bestseller | 4.6 | 1289 | 6869 | Created by George Smarts | Nov-22 | English | $9.99 | 8h 54m total length | https://www.udemy.com/course/looker-learning-tutorial-for-beginners/ | George Smarts | Use software to become a productivity superstar | 4.5 | 8345 | 63010 | Welcome! I am here to help you learn quickly how to use Looker and LookML ------------------------------------------------------------------------ Beginners welcome: no need to know anything about Looker and LookML! Looker can be challenging to learn on your own without guidance from an experienced trainer. In this course, I will walk you through every step of using Looker and LookML in an easy to undertand way for the absolute beginner. This course will give you a deep understanding of Looker's functionality by using hands-on, contextual examples designed to showcase why Looker is awesome and how how to use it for any project. In this Looker course you will learn: · How to setup a free training account with Looker · Get familiar with the Looker interface · How to build Looks in Looker · How to edit a Look · How to build a dashboard · The different elements of LookML · How to create a custom dimension · How to create a view from table · Understand the different dimension types · How to structure your project · How to use Explores · How to utilize the formal Looker documentation · How to create a model in the Development environment · The interface of the Development environment ... and much, much more! Enroll today and enjoy: Lifetime access to the course and all future updates 9 hours of high quality, up to date video lectures Practical Looker course with step by step instructions on how to implement the different features Thanks again for checking out my course and I look forward to seeing you in the classroom | https://www.udemy.com/course/looker-learning-tutorial-for-beginners/#instructor-1 | Hello! I am a Project Manager and data geek with 10 years worth of experience working for Fortune 500 companies. The past decade was a really challenging and fulfilling one that allowed me to work on multiple complex international projects in the IT industry. I have experience with most of the Project Management software tools on the market and would love to share my knowledge and best practices from the industry. Here are some of the topics that I love teaching and why: - Project Management software tools - I love finding new useful features and utilizing them to increase the collaboration and success of projects. I have used all the well known Project Management software tools on the market. - Data related topics - I have led and participated in dozens of different Data related projects and initiatives. I am a data geek and love to share my knowledge on the topic. - Productivity software tools - one of my secrets to success is that over the years I have used software tools to increase my productivity and motivation to get things done. The aim of my courses is to help you achieve one of the following: - Get the next promotion at work by utilizing software tools to set yourself apart from the competition - Become better at managing projects by utilizing industry best pracrices - Become better at managing the collaboration within your team - Make your resume standout by adding trending skills - Improve the operations of your own business My mantra is teaching by doing. Let's get going! | Misc | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
A deep understanding of deep learning (with Python intro) | Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems. | 4.8 | 1284 | 11740 | Created by Mike X Cohen | Oct-22 | English | $9.99 | 57h 19m total length | https://www.udemy.com/course/deeplearning_x/ | Mike X Cohen | Neuroscientist, writer, professor | 4.6 | 36367 | 187910 | Deep learning is increasingly dominating technology and has major implications for society. From self-driving cars to medical diagnoses, from face recognition to deep fakes, and from language translation to music generation, deep learning is spreading like wildfire throughout all areas of modern technology. But deep learning is not only about super-fancy, cutting-edge, highly sophisticated applications. Deep learning is increasingly becoming a standard tool in machine-learning, data science, and statistics. Deep learning is used by small startups for data mining and dimension reduction, by governments for detecting tax evasion, and by scientists for detecting patterns in their research data. Deep learning is now used in most areas of technology, business, and entertainment. And it's becoming more important every year. How does deep learning work? Deep learning is built on a really simple principle: Take a super-simple algorithm (weighted sum and nonlinearity), and repeat it many many times until the result is an incredibly complex and sophisticated learned representation of the data. Is it really that simple? mmm OK, it's actually a tiny bit more complicated than that 😉 but that's the core idea, and everything else -- literally everything else in deep learning -- is just clever ways of putting together these fundamental building blocks. That doesn't mean the deep neural networks are trivial to understand: there are important architectural differences between feedforward networks, convolutional networks, and recurrent networks. Given the diversity of deep learning model designs, parameters, and applications, you can only learn deep learning -- I mean, really learn deep learning, not just have superficial knowledge from a youtube video -- by having an experienced teacher guide you through the math, implementations, and reasoning. And of course, you need to have lots of hands-on examples and practice problems to work through. Deep learning is basically just applied math, and, as everyone knows, math is not a spectator sport! What is this course all about? Simply put: The purpose of this course is to provide a deep-dive into deep learning. You will gain flexible, fundamental, and lasting expertise on deep learning. You will have a deep understanding of the fundamental concepts in deep learning, so that you will be able to learn new topics and trends that emerge in the future. Please note: This is not a course for someone who wants a quick overview of deep learning with a few solved examples. Instead, this course is designed for people who really want to understand how and why deep learning works; when and how to select metaparameters like optimizers, normalizations, and learning rates; how to evaluate the performance of deep neural network models; and how to modify and adapt existing models to solve new problems. You can learn everything about deep learning in this course. In this course, you will learn Theory: Why are deep learning models built the way they are? Math: What are the formulas and mechanisms of deep learning? Implementation: How are deep learning models actually constructed in Python (using the PyTorch library)? Intuition: Why is this or that metaparameter the right choice? How to interpret the effects of regularization? etc. Python: If you're completely new to Python, go through the 8+ hour coding tutorial appendix. If you're already a knowledgeable coder, then you'll still learn some new tricks and code optimizations. Google-colab: Colab is an amazing online tool for running Python code, simulations, and heavy computations using Google's cloud services. No need to install anything on your computer. Unique aspects of this course Clear and comprehensible explanations of concepts in deep learning. Several distinct explanations of the same ideas, which is a proven technique for learning. Visualizations using graphs, numbers, and spaces that provide intuition of artificial neural networks. LOTS of exercises, projects, code-challenges, suggestions for exploring the code. You learn best by doing it yourself! Active Q&A forum where you can ask questions, get feedback, and contribute to the community. 8+ hour Python tutorial. That means you don't need to master Python before enrolling in this course. So what are you waiting for?? Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up. I hope to see you soon in the course! Mike | https://www.udemy.com/course/deeplearning_x/#instructor-1 | I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations. But you're here because of my teaching, so let me tell you about that: I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way. I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style. Over 120,000 students have watched over 7,500,000 minutes of my courses. Come find out why! I have several free courses that you can enroll in. Try them out! You got nothing to lose 😉 ------------------------- By popular request, here are suggested course progressions for various educational goals: MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python Applied linear algebra: Complete Linear Algebra; Dimension Reduction Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing | Deep Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Mathematical Foundation For Machine Learning and AI | Learn the core mathematical concepts for machine learning and learn to implement them in R and python | 3.9 | 1231 | 7251 | Created by Eduonix Learning Solutions, Eduonix-Tech . | Dec-18 | English | $9.99 | 4h 16m total length | https://www.udemy.com/course/mathematical-foundation-for-machine-learning-and-ai/ | Eduonix Learning Solutions | 1+ Million Students Worldwide | 200+ Courses | 3.9 | 90535 | 1 | Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with the self-driving cars, medical diagnosis and even betting humans at strategy games like Go and Chess. The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML. The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts. The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory. Linear Algebra – Linear algebra notation is used in Machine Learning to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating. It covers topics such as: Scalars, Vectors, Matrices, Tensors Matrix Norms Special Matrices and Vectors Eigenvalues and Eigenvectors Multivariate Calculus – This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance. It covers topics such as: Derivatives Integrals Gradients Differential Operators Convex Optimization Probability Theory – The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course. It covers topics such as: Elements of Probability Random Variables Distributions Variance and Expectation Special Random Variables The course also includes projects and quizzes after each section to help solidify your knowledge of the topic as well as learn exactly how to use the concepts in real life. At the end of this course, you will not have not only the knowledge to build your own algorithms, but also the confidence to actually start putting your algorithms to use in your next projects. Enroll now and become the next AI master with this fundamentals course! | https://www.udemy.com/course/mathematical-foundation-for-machine-learning-and-ai/#instructor-1 | Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade. We aim to teach technology the way it is used in industry and professional world. We have professional team of trainers for technologies ranging from Mobility, Web to Enterprise and Database and Server Administration. | Machine Learning | >=3 | Below 10K | Below 10K | >=3 | Below 1 Lakh | Million+ | ||||||||||||||||||
Financial Engineering and Artificial Intelligence in Python | Financial Analysis, Time Series Analysis, Portfolio Optimization, CAPM, Algorithmic Trading, Q-Learning, and MORE! | Bestseller | 4.8 | 1227 | 5923 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $79.99 | 21h 28m total length | https://www.udemy.com/course/ai-finance/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model ARIMA and SARIMA Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) Algorithmic trading (VIP only) Statistical Factor Models (VIP only) Regime Detection with Hidden Markov Models (VIP only) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Regression models Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) *** Algorithmic trading (trend-following, machine learning, and Q-learning-based strategies) Statistical factor models Regime detection and modeling volatility clustering with HMMs We will learn about the greatest flub made in the past decade by marketers posing as "machine learning experts" who promise to teach unsuspecting students how to "predict stock prices with LSTMs". You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense. It is a lesson in how not to apply AI in finance. As the author of ~30 courses in machine learning, deep learning, data science, and artificial intelligence, I couldn't help but wander into the vast and complex world of financial engineering. This course is for anyone who loves finance or artificial intelligence, and especially if you love both! Whether you are a student, a professional, or someone who wants to advance their career - this course is for you. Thanks for reading, I will see you in class! Suggested Prerequisites: Matrix arithmetic Probability Decent Python coding skills Numpy, Matplotlib, Scipy, and Pandas (I teach this for free, no excuses!) WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/ai-finance/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Artificial Intelligence | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Time Series Analysis, Forecasting, and Machine Learning | Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting | Bestseller | 4.7 | 1221 | 4526 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $79.99 | 22h 51m total length | https://www.udemy.com/course/time-series-analysis/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Hello friends! Welcome to Time Series Analysis, Forecasting, and Machine Learning in Python. Time Series Analysis has become an especially important field in recent years. With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value. COVID-19 has shown us how forecasting is an essential tool for driving public health decisions. Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time. Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more. We will cover techniques such as: ETS and Exponential Smoothing Holt's Linear Trend Model Holt-Winters Model ARIMA, SARIMA, SARIMAX, and Auto ARIMA ACF and PACF Vector Autoregression and Moving Average Models (VAR, VMA, VARMA) Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests) Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks) GRUs and LSTMs for Time Series Forecasting We will cover applications such as: Time series forecasting of sales data Time series forecasting of stock prices and stock returns Time series classification of smartphone data to predict user behavior The VIP version of the course will cover even more exciting topics, such as: AWS Forecast (Amazon's state-of-the-art low-code forecasting API) GARCH (financial volatility modeling) FB Prophet (Facebook's time series library) So what are you waiting for? Signup now to get lifetime access, a certificate of completion you can show off on your LinkedIn profile, and the skills to use the latest time series analysis techniques that you cannot learn anywhere else. Thanks for reading, and I'll see you in class! UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/time-series-analysis/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Learn DBT from Scratch | Complete guide to Learning DBT including connecting it to a Data Warehouse | 4.5 | 1216 | 6307 | Created by Jeremy Holtzman | Aug-20 | English | $9.99 | 3h 3m total length | https://www.udemy.com/course/learn-dbt-from-scratch/ | Jeremy Holtzman | Data Engineer / Founder | 4.5 | 1228 | 6412 | What you'll learn Welcome to this course, Learn DBT from Scratch. DBT lets you build a system of transformations on your data, with tests, scheduled runs, multiple environments, flexibility, and more all without needing a team of engineers to set up and manage your workflow. By the end of this course, you will have: set up DBT locally and on the cloud connected DBT to Snowflake (or a data warehouse of your choice) create your own SQL transformations on data test your transformations snapshot your data to keep track of how your data changes over time learn DBT best practices In this course, you'll be presented with the summarized information you need so that you can quickly get DBT implemented in your data pipeline (or in a brand new, data warehouse). Why you should learn DBT DBT is not one of the first technical skills most Data Scientists or Analysts think to learn. It’s not as exciting as machine learning algorithms, and it’s not as easy to show off as a fancy data visualization. But DBT is an absolutely fundamental skill for any Data Scientist or Analyst due to all of its capabilities. Because DBT is so flexible, there are almost an endless amount of ways you can integrate DBT into your data architecture. Some features that DBT provides you that all Data Scientists and Analysts should be using in their work include: Creating consistent aggregations for your analysis in a single location Consistently testing your transformations and underlying data Running your data transformations on a schedule Test your code in a DEV environment About DBT DBT is pioneering modern analytics engineering. DBT applies the principles of software engineering to analytics code, an approach that dramatically increases your leverage as a data analyst. They believe that data analysts are the most valuable employees of modern, data-driven businesses and they build tools that empower analysts to own the entire analytics engineering workflow. | https://www.udemy.com/course/learn-dbt-from-scratch/#instructor-1 | I've been working with data for the last 6 years, and I've recently started sharing the skills I've learned along the way in these online courses. I love helping to simplify all of the upfront time it takes to learn these super useful data tools, and I hope you enjoy the different courses I've put together to add to your toolbox of skills. | Misc | Founder/Entrepreneur | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Deploying AI & Machine Learning Models for Business | Python | Learn to build Machine Learning, Deep Learning & NLP Models & Deploy them with Docker Containers (DevOps) (in Python) | 4.5 | 1207 | 6350 | Created by UNP United Network of Professionals | Jun-18 | English | $9.99 | 4h 12m total length | https://www.udemy.com/course/deploy-data-science-nlp-models-with-docker-containers/ | UNP United Network of Professionals | Publishing top-notch data science learning materials | 4.5 | 1368 | 25299 | Machine Learning, as we know it is the new buzz word in the industry today. This is practiced in every sector of business imaginable to provide data-driven solutions to complex business problems. This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos. This is an extensive and well-thought course created & designed by UNP's elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry which is summarized the below sentence : "I HAVE THE MACHINE LEARNING MODEL, IT IS WORKING AS EXPECTED !! NOW, WHAT ?????" This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers which eventually will expose your model as a service (API) which can be used by all who wish for it. At the end of this course, you will be able to: Learn about Docker, Docker Files, Docker Containers Learn Flask Basics & Application Program Interface (API) Build a Random Forest Model and deploy it. Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it. Build an API for Image Processing and Recognition with a Deep Learning Model under the hood (Convolutional Neural Network: CNN) This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them. | https://www.udemy.com/course/deploy-data-science-nlp-models-with-docker-containers/#instructor-1 | We are a team of working professionals from around the globe ,about 30 strong, coming from various spheres of the Data Science Universe,each bringing in a unique set of skills which we have acquired through years of experience in almost every domain of Business.The Professionals in UNP are unified by a single common goal to minimise the entry barrier to quality education at every stage of one’s life and we strongly believe that knowledge should be shared in its truest form to transcend.We are committed to provide quality education in the realms of Data Sciences coupling it with IoT and Cloud Computing, DevOps, Quantum Computing & Blockchain At UNP- R&D emerging Tech are being nurtured and applied to create the first Decentralised Education Ecosystem, as we believe democratisation of knowledge & education is the Future. | Machine Learning | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Neural Networks in Python: Deep Learning for Beginners | Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python | 4.5 | 1199 | 123113 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 9h 15m total length | https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in Python, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create a predictive model using Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 - Python basics This part gets you started with Python. This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Part 2 - Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in Python In this part you will learn how to create ANN models in Python. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 - Data Preprocessing In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. Part 5 - Classic ML technique - Linear Regression This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use Python for Deep Learning? Understanding Python is one of the valuable skills needed for a career in Deep Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Deep Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Full stack web development and AI with Python (Django) | HTML, CSS, JavaScript, Python, Django, Pandas, Sklearn, Keras, Git, Linux - Full stack web development /data science/ AI | 4.4 | 1191 | 8093 | Created by John Harper | Jul-20 | English | $11.99 | 39h 29m total length | https://www.udemy.com/course/unaicorn/ | John Harper | Cambridge Python and Machine Learning Engineer/entrepreneur | 4.3 | 1730 | 13227 | MASTERCLASS, WORLD CLASS COURSE - FULL STACK WEB DEVELOPMENT, MACHINE LEARNING + AI INTEGRATIONS Master practical and theoretical concepts This full stack web development, Django and AI combination course leads you through a complete range of software skills and languages, skilling you up to be an incredibly on-demand developer. The combination of being able to create full-stack websites AND machine learning and AI models is very rare - something referred to as a unAIcorn. This is exactly what you will be able to do by the end of this course. Why you need this course Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your own tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unAIcorn. It won't matter if you're a complete beginner to software or a seasoned veteran. This course will fill all the gaps in between. I will be there with you through your complete learning experience. What you will get out of this course I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to start coding your own programs, solving problems that inspire you and instilling the 'developer's mindset' of problem solving into you. I don't just throw you in at the deep end - I provide you with the resources to learn and develop what you need at a pace that works for you and then help you stroll through to the finish line. Studies have shown that to learn effectively from online courses tutorials should last around ten minutes each. Therefore to maximise your learning experience all of the lectures in this course have been created around this amount of time or less. My course integrates all of the aspects required to get you on the road becoming a successful web, software and machine learning developer. I teach and I preach, with live, practical exercises and walkthroughs throughout each of the sections. By paying a small cost for this course I believe you will get your value back, with a lot more by the time you have completed it. Ask yourself - how much is mastering a full spectrum of skills in some of of the most exciting areas of software worth to you? How long will it take? Although everyone is different, on average it has taken existing students between 1 - 6 months to complete the course, whilst developing their skills and knowledge along the way. It's best not to speed through the content, and instead go through a handful of lectures, try out the concepts by coding, yourself, and move on once you feel you've grasped the basics of those lectures. Who this is not for This course is not for anyone looking for a one-click fix. Although I provide you with a path walked enough times that it can be a smooth journey it still requires time and effort from you to make it happen. If you're not interested in putting in your energy to truly better yours skills then this may not be the right course for you. Is there a money back guarantee if I'm not happy? Absolutely. I am confident that my course will bring you more value than you spend on the course. As one of the top featured Udemy Instructors my motto is 'your success is my success'. If within the first 30 days you feel my course is not going to help you to achieve your goals then you get a no questions asked, full discount. What materials are included? The majority of my lectures I have chosen to be as video so that you can hear me and see my workings when we're going through each and every area of the course. I include a vast array of practical projects that you can then use in the future to showcase your skills as you develop them, along with introductory clips and quizzes in each section to ensure that you're grasping the concepts effectively. I will be consistently adding more content and resources to the course as time goes by. Keep checking back here if you're not sure right now and feel free to send me a message with any questions or requests you may have. So go ahead and click the 'Buy now' button when you feel ready on your screen. I look forward to seeing you in the course. | https://www.udemy.com/course/unaicorn/#instructor-1 | I am a Machine Learning Engineer, Python Programmer and award winning tech entrepreneur. Trained at Cambridge University, Accelerate Cambridge and a recent scholar at the Prestigious Pi School in Rome for Artificial Intelligence. I have a passion for teaching others and enabling them to create amazing things with the power of Python and AI. I believe that we as a community can provide much better resources to help people to understand, master, and enjoy programming, and AI Engineering. That's why I'm here. My ambition is to provide the best courses out there on Python and AI. As a result I'm constantly improving my courses - adding new content, responding to feedback and making sure that my students get the best possible learning experience. Any questions? Feel free to leave me a message. | Python | Founder/Entrepreneur | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
PySpark & AWS: Master Big Data With PySpark and AWS | Learn how to use Spark, Pyspark AWS, Spark applications, Spark EcoSystem, Hadoop and Mastering PySpark | 4.5 | 1183 | 8402 | Created by AI Sciences, AI Sciences Team | Nov-22 | English | $9.99 | 16h 28m total length | https://www.udemy.com/course/pyspark-aws-master-big-data-with-pyspark-and-aws/ | AI Sciences | AI Experts & Data Scientists |4+ Rated | 160+ Countries | 4.5 | 2414 | 40710 | Comprehensive Course Description: The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you’ll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you’ll learn how to execute end-to-end workflows using PySpark. Right through the course, you’ll be using PySpark for performing data analysis. You’ll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you’ll explore the transformations and actions that can be performed on the data using Spark RDDs and dataframes. You’ll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You’ll use the Databricks environment for running the Spark scripts and explore it as well. Finally, you’ll have a taste of Spark with AWS cloud. You’ll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data. How Is This Course Different? In this Learning by Doing course, every theoretical explanation is followed by practical implementation. The course ‘PySpark & AWS: Master Big Data With PySpark and AWS’ is crafted to reflect the most in-demand workplace skills. This course will help you understand all the essential concepts and methodologies with regards to PySpark. The course is: • Easy to understand. • Expressive. • Exhaustive. • Practical with live coding. • Rich with the state of the art and latest knowledge of this field. As this course is a detailed compilation of all the basics, it will motivate you to make quick progress and experience much more than what you have learned. At the end of each concept, you will be assigned Homework/tasks/activities/quizzes along with solutions. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations. High-quality video content, in-depth course material, evaluating questions, detailed course notes, and informative handouts are some of the perks of this course. You can approach our friendly team in case of any course-related queries, and we assure you of a fast response. The course tutorials are divided into 140+ brief videos. You’ll learn the concepts and methodologies of PySpark and AWS along with a lot of practical implementation. The total runtime of the HD videos is around 16 hours. Why Should You Learn PySpark and AWS? PySpark is the Python library that makes the magic happen. PySpark is worth learning because of the huge demand for Spark professionals and the high salaries they command. The usage of PySpark in Big Data processing is increasing at a rapid pace compared to other Big Data tools. AWS, launched in 2006, is the fastest-growing public cloud. The right time to cash in on cloud computing skills—AWS skills, to be precise—is now. Course Content: The all-inclusive course consists of the following topics: 1. Introduction: a. Why Big Data? b. Applications of PySpark c. Introduction to the Instructor d. Introduction to the Course e. Projects Overview 2. Introduction to Hadoop, Spark EcoSystems, and Architectures: a. Hadoop EcoSystem b. Spark EcoSystem c. Hadoop Architecture d. Spark Architecture e. PySpark Databricks setup f. PySpark local setup 3. Spark RDDs: a. Introduction to PySpark RDDs b. Understanding underlying Partitions c. RDD transformations d. RDD actions e. Creating Spark RDD f. Running Spark Code Locally g. RDD Map (Lambda) h. RDD Map (Simple Function) i. RDD FlatMap j. RDD Filter k. RDD Distinct l. RDD GroupByKey m. RDD ReduceByKey n. RDD (Count and CountByValue) o. RDD (saveAsTextFile) p. RDD (Partition) q. Finding Average r. Finding Min and Max s. Mini project on student data set analysis t. Total Marks by Male and Female Student u. Total Passed and Failed Students v. Total Enrollments per Course w. Total Marks per Course x. Average marks per Course y. Finding Minimum and Maximum marks z. Average Age of Male and Female Students 4. Spark DFs: a. Introduction to PySpark DFs b. Understanding underlying RDDs c. DFs transformations d. DFs actions e. Creating Spark DFs f. Spark Infer Schema g. Spark Provide Schema h. Create DF from RDD i. Select DF Columns j. Spark DF with Column k. Spark DF with Column Renamed and Alias l. Spark DF Filter rows m. Spark DF (Count, Distinct, Duplicate) n. Spark DF (sort, order By) o. Spark DF (Group By) p. Spark DF (UDFs) q. Spark DF (DF to RDD) r. Spark DF (Spark SQL) s. Spark DF (Write DF) t. Mini project on Employees data set analysis u. Project Overview v. Project (Count and Select) w. Project (Group By) x. Project (Group By, Aggregations, and Order By) y. Project (Filtering) z. Project (UDF and With Column) aa. Project (Write) 5. Collaborative filtering: a. Understanding collaborative filtering b. Developing recommendation system using ALS model c. Utility Matrix d. Explicit and Implicit Ratings e. Expected Results f. Dataset g. Joining Dataframes h. Train and Test Data i. ALS model j. Hyperparameter tuning and cross-validation k. Best model and evaluate predictions l. Recommendations 6. Spark Streaming: a. Understanding the difference between batch and streaming analysis. b. Hands-on with spark streaming through word count example c. Spark Streaming with RDD d. Spark Streaming Context e. Spark Streaming Reading Data f. Spark Streaming Cluster Restart g. Spark Streaming RDD Transformations h. Spark Streaming DF i. Spark Streaming Display j. Spark Streaming DF Aggregations 7. ETL Pipeline a. Understanding the ETL b. ETL pipeline Flow c. Data set d. Extracting Data e. Transforming Data f. Loading data (Creating RDS) g. Load data (Creating RDS) h. RDS Networking i. Downloading Postgres j. Installing Postgres k. Connect to RDS through PgAdmin l. Loading Data 8. Project – Change Data Capture / Replication On Going a. Introduction to Project b. Project Architecture c. Creating RDS MySql Instance d. Creating S3 Bucket e. Creating DMS Source Endpoint f. Creating DMS Destination Endpoint g. Creating DMS Instance h. MySql WorkBench i. Connecting with RDS and Dumping Data j. Querying RDS k. DMS Full Load l. DMS Replication Ongoing m. Stoping Instances n. Glue Job (Full Load) o. Glue Job (Change Capture) p. Glue Job (CDC) q. Creating Lambda Function and Adding Trigger r. Checking Trigger s. Getting S3 file name in Lambda t. Creating Glue Job u. Adding Invoke for Glue Job v. Testing Invoke w. Writing Glue Shell Job x. Full Load Pipeline y. Change Data Capture Pipeline After the successful completion of this course, you will be able to: ● Relate the concepts and practicals of Spark and AWS with real-world problems. ● Implement any project that requires PySpark knowledge from scratch. ● Know the theory and practical aspects of PySpark and AWS. Who this course is for: ● People who are beginners and know absolutely nothing about PySpark and AWS. ● People who want to develop intelligent solutions. ● People who want to learn PySpark and AWS. ● People who love to learn the theoretical concepts first before implementing them using Python. ● People who want to learn PySpark along with its implementation in realistic projects. ● Big Data Scientists. ● Big Data Engineers. | https://www.udemy.com/course/pyspark-aws-master-big-data-with-pyspark-and-aws/#instructor-1 | We are a group of experts, PhDs and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. We decided to produce a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence, and Data Science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience. Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science. | Big Data/Data Engineer | Data Scientist | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Convolutional Neural Networks in Python: CNN Computer Vision | Python for Computer Vision & Image Recognition - Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2 | 4.5 | 1169 | 120022 | Created by Start-Tech Academy | Nov-22 | English | $10.99 | 7h 41m total length | https://www.udemy.com/course/cnn-for-computer-vision-with-keras-and-tensorflow-in-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right? You've found the right Convolutional Neural Networks course! After completing this course you will be able to: Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in Python using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 1,300,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 (Section 2)- Python basics This part gets you started with Python. This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Part 2 (Section 3-6) - ANN Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 (Section 7-11) - Creating ANN model in Python In this part you will learn how to create ANN models in Python. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 (Section 12) - CNN Theoretical Concepts In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model. Part 5 (Section 13-14) - Creating CNN model in Python In this part you will learn how to create CNN models in Python. We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part. Part 6 (Section 15-18) - End-to-End Image Recognition project in Python In this section we build a complete image recognition project on colored images. We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition). By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use Python for Deep Learning? Understanding Python is one of the valuable skills needed for a career in Deep Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/cnn-for-computer-vision-with-keras-and-tensorflow-in-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Computer Vision | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
U&P AI - Natural Language Processing (NLP) with Python | Become an NLP Engineer by creating real projects using Python, semantic search, text mining and search engines! | 4 | 1145 | 13874 | Created by Abdulhadi Darwish | Dec-20 | English | $9.99 | 5h 49m total length | https://www.udemy.com/course/understand-and-practice-ai-natural-language-processing-in-python/ | Abdulhadi Darwish | Machine Learning Engineer and Software Developer | 4 | 1145 | 14417 | -- UPDATED -- (NEW LESSONS ARE NOT IN THE PROMO VIDEO) THIS COURSE IS FOR BEGINERS OR INTERMEDIATES, IT IS NOT FOR EXPERTS This course is a part of a series of courses specialized in artificial intelligence : Understand and Practice AI | https://www.udemy.com/course/understand-and-practice-ai-natural-language-processing-in-python/#instructor-1 | . My name is Abdulhadi Darwish, I am a machine learning engineer, and I have studied at the Faculty of Information Technology Engineering of Damascus University Department of Artificial Intelligence. I thrive for what makes people's lives easier, more fun, and more convenient, I'm interested in games, mobile and web applications, education, AI, Machine Learning, and Whatever doesn't destroy me and makes me stronger. I have done courses in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Data Science, Game Development, from other Universities like Stanford, Washington, Michigan and National Research University Higher School of Economics (HSE) online. I have experience in Computer Science, programming languages, algorithms, data structures, and I've developed many applications in android, web, and some games that use artificial intelligence and machine learning techniques using the Unity game engine. | NLP | Engineer/Developer | >=4 | Below 10K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Data Science: Python for Data Analysis 2022 Full Bootcamp | Learn and build your Python Programming skills from the ground up in addition to Python Data Science libraries and tools | 4.1 | 1133 | 132450 | Created by Ahmed Ibrahim, SDE Arts | Octavo | Oct-22 | English | $9.99 | 6h 10m total length | https://www.udemy.com/course/mastering-python-data-handling-analysis-and-visualization/ | Ahmed Ibrahim | Software Engineer | Data Science Professional | Instructor | 4.3 | 7448 | 460367 | Hello and welcome to Data Science: Python for Data Analysis 2022 Full Bootcamp. Data science is a huge field, and one of the promising fields that is spreading in a fast way. Also, it is one of the very rewarding, and it is increasing in expansion day by day, due to its great importance and benefits, as it is the future. Data science enables companies to measure, track, and record performance metrics for facilitating and enhancing decision making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability. And the employment of data science and its tools depends on the purpose you want from them. For example, using data science in health care is very different from using data science in finance and accounting, and so on. And I’ll show you the core libraries for data handling, analysis and visualization which you can use in different areas. One of the most powerful programming languages that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data. This course will cover: Python tools for Data Analysis Python Basics Python Fundamentals Python Object-Oriented Advanced Python Foundations Data Handling with Python Numerical Python(NumPy) Data Analysis with Pandas Data Visualization with Matplotlib Advanced Graphs with Seaborn Instructor QA Support and Help HD Video Training + Working Files + Resources + QA Support. In this course, you will learn how to code in Python from the beginning and then you will master how to deal with the most famous libraries and tools of the Python language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that, the necessary decisions are taken by companies. I am Ahmed Ibrahim, a software engineer and Instructor and I have taught more than 200,000 engineers and developers around the world in topics related to programming languages and their applications, and in this course, we will dive deeply into the core Python fundamentals, Advanced Foundations, Data handling libraries, Numerical Python, Pandas, Matplotlib and finally Seaborn. I hope that you will join us in this course to master the Python language for data analysis and Visualization like professionals in this field. We have a lot to cover in this course. Let’s get started! | https://www.udemy.com/course/mastering-python-data-handling-analysis-and-visualization/#instructor-1 | Software Engineer | Data Science Professional | Course Creator/Developer I taught for more than 300,000 developers and engineers from over 175 countries around the world. - Programming Languages: Python, R, JavaScript, Java and Go. - Data Science: Data Analysis and Visualization tools and Libraries with Python, R, SQL and Spark. - Databases: Relational and Non-Relational. - Applied experience with many programming languages and tools, also a proficient knowledge and experience in Software Engineering and Data Science with skills to analyze, design and develop. - Bachelor's degree of Electrical, Communications and Computer Engineering. I'm always have a passion to develop my work and I like to simplify and clarify Software and Data Science skills and tools, and sharing my skills and expertise with others via high quality, and direct to point video training courses. | Python | Engineer/Developer | >=4 | Below 10K | >=1 Lakh | >=4 | Below 10 K | >=4.5 Lakh | |||||||||||||||||
Python & Introduction to Data Science | Learn the basics of Python and the most important Data Science libraries with this step by step guide! | 4.4 | 1098 | 45749 | Created by AI 4 MY | Oct-18 | English | $9.99 | 9h 8m total length | https://www.udemy.com/course/python-introduction-to-data-science/ | AI 4 MY | AI Educational Company | 4.4 | 1098 | 45749 | Python is the most important language in the field of data, and its libraries for analysis and modeling are the most relevant tools to use. In this course we will start building the basics of Python and then going to deepen the fundamental libraries like Numpy, Pandas, and Matplotlib. The four main features of this course are: 1. Clear and simplified language, suitable for everyone 2. Practical and efficient 3. Examples, illustrations and demonstrations with relative explanations 4. Continuous updating of contents and exercises | https://www.udemy.com/course/python-introduction-to-data-science/#instructor-1 | Hi! We are ai4my, an innovative educational company focused on new technologies and Data Mining. Our main goal is to offer you practical and reproducible contents through a simple and clear language, accessible to everyone, from the beginners to the most advanced IT experts. Can't wait to see you in class! | Python | >=4 | Below 10K | >=45K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
MLOps Fundamentals: CI/CD/CT Pipelines of ML with Azure Demo | MLOps fundamentals of Continuous Integration & Continuous Delivery (CI/CD) using Azure DevOps & Azure Machine Learning | 4.2 | 1092 | 5497 | Created by J Garg - Real Time Learning | Jul-22 | English | $9.99 | 2h 53m total length | https://www.udemy.com/course/mlops-course/ | J Garg - Real Time Learning | Data Engineering, Analytics and Cloud Trainer | 4.3 | 5750 | 34913 | Important Note: The intention of this course is to teach MLOps fundamentals and not Azure ML. Azure demo section is included as a proof to show the working of an end-to-end MLOps project. All the codes involved in pipeline are well explained though. "MLOps is a culture with set of principles, guidelines defined in machine learning world for smooth implementation and productionization of Machine learning models." Data scientists have been experimenting with machine learning models from long time, but to provide the real business value, they must be operationalized i.e. push the models to production. Unfortunately, due to the current challenges and an non systemization in ML lifecycle, 80% of the models never make it to production and remain stagnated as an academic experiment only. Machine Learning Operations (MLOps), emerged as a solution to the problem, is a new culture in the market and a rapidly growing space that encompasses everything required to deploy a machine learning model into production. As per the tech talks in market, 2023 is the year of MLOps and would become the mandate skill set for Enterprise ML projects. What's included in the course ? MLOps core basics and fundamentals. What were the challenges in the traditional machine learning lifecycle management. How MLOps is addressing those issues while providing more flexibility and automation in the ML process. Standards and principles on which MLOps is based upon. Continuous integration (CI), Continuous delivery (CD) and Continuous training (CT) pipelines in MLOps. Various maturity levels associated with MLOps. MLOps tools stack and MLOps platforms comparisons. Quick crash course on Azure Machine learning components. An end-to-end CI/CD MLOps pipeline for a case study in Azure using Azure DevOps & Azure Machine learning. | https://www.udemy.com/course/mlops-course/#instructor-1 | All of our courses are made keeping in mind the Real-time implementation of Big data, Machine Learning and Cloud technologies in Live Projects. We make courses which majorly consist of Hands-On & Practicals. All our courses contain a detailed knowledge of a technology from Scratch to Advance level. Course's lectures explain the codes in such a way that even a Non-technical person can understand. | Azure | Engineer/Developer | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Python for Data Science and Machine Learning beginners | A Complete Machine learning Bootcamp learn Numpy, Pandas, Matplotlib, Stats, Plotly , EDA , Scikit-learn and more! | 4 | 1069 | 16041 | Created by Jay Bhatt | Apr-21 | English | $10.99 | 7h 40m total length | https://www.udemy.com/course/python-for-data-science-and-machine-learning-beginners/ | Jay Bhatt | Data Scientist by Profession Instructor by Passion | 3.8 | 3446 | 28533 | Hi all Its Jay I am a data scientist by profession and Instructor by passion I have around 4 years of experience as data scientist, I started my career as analyst as gradually moved to data scientist hence I can understand what are programming prerequisites for data scientist. This course is created for absolute beginners of data science and machine learning. It covers all aspect of python languages required in data science machine learning and deep learning. | https://www.udemy.com/course/python-for-data-science-and-machine-learning-beginners/#instructor-1 | Hi my name is Jay Having 5 years of experience in a leading Data Science Company, I have completed my masters degree adv mathematics and FEM . I love making educational videos and content. check out my you-tube channel and all udamy tutorial and stay updated with new techniques of data science and machine learning. Hope you will enjoy this lovely journey of Data science and machine learning. | Machine Learning | Data Scientist | >=4 | Below 10K | >=15K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Machine Learning & Data Science A-Z: Hands-on Python 2022 | Learn NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Scipy and develop Machine Learning Models in Python | 4.4 | 1065 | 68703 | Created by Navid Shirzadi | Jan-22 | English | $11.99 | 14h 27m total length | https://www.udemy.com/course/data-science-machine-learning-a-z-hands-on-python/ | Navid Shirzadi | Data Analyst - Optimization Expert | 4.5 | 1523 | 71215 | Are you interested in data science and machine learning, but you don't have any background, and you find the concepts confusing? Are you interested in programming in Python, but you always afraid of coding? I think this course is for you! Even if you are familiar with machine learning, this course can help you to review all the techniques and understand the concept behind each term. This course is completely categorized, and we don't start from the middle! We actually start from the concept of every term, and then we try to implement it in Python step by step. The structure of the course is as follows: Chapter1: Introduction and all required installations Chapter2: Useful Machine Learning libraries (NumPy, Pandas & Matplotlib) Chapter3: Preprocessing Chapter4: Machine Learning Types Chapter5: Supervised Learning: Classification Chapter6: Supervised Learning: Regression Chapter7: Unsupervised Learning: Clustering Chapter8: Model Tuning Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section. Remember! That this course is created for you with any background as all the concepts will be explained from the basics! Also, the programming in Python will be explained from the basic coding, and you just need to know the syntax of Python. | https://www.udemy.com/course/data-science-machine-learning-a-z-hands-on-python/#instructor-1 | My name is Navid Shirzaid and I am super excited that you are here to read this section! I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence. To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you! | Machine Learning | >=4 | Below 10K | >=50K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Learn Data Science Deep Learning, Machine Learning NLP & R | Learn Data Science, Deep Learning, Machine Learning, Natural Language Processing, R and Python Language with libraries | 4.6 | 1064 | 6085 | Created by Cinnamon TechX | Aug-22 | English | $11.99 | 70h 32m total length | https://www.udemy.com/course/learn-data-science-deep-learning-machine-learning-nlp-r/ | Cinnamon TechX | Providing Breakthrough Learning | 4.5 | 1784 | 13367 | || DATA SCIENCE || Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. What Does a Data Scientist Do? In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business. Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms. Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. 4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions. The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies. Where Do You Fit in Data Science? Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data. Data Scientist Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning Data Analyst Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization Data Engineer Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying. Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop) Data Science Career Outlook and Salary Opportunities Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not. Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases. Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength. || DEEP LEARNING || Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. How does deep learning attain such impressive results? In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful: Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less. Examples of Deep Learning at Work Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells. Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines. Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications. What's the Difference Between Machine Learning and Deep Learning? Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically. Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network. A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. || MACHINE LEARNING || What is the definition of machine learning? Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on. In all of these instances, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth. WHY IS MACHINE LEARNING SO SUCCESSFUL? While machine learning is not a new technique, interest in the field has exploded in recent years. This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. What's made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems. But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses. Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft. As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained. These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further. As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android. ||Natural Language Processing|| Large volumes of textual data Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks. For example, NLP makes it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important. Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently. Structuring a highly unstructured data source Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages. While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics. How does NLP work? Breaking down the elemental pieces of language Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications. Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before. In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning. These underlying tasks are often used in higher-level NLP capabilities, such as: Content categorization. A linguistic-based document summary, including search and indexing, content alerts and duplication detection. Topic discovery and modeling. Accurately capture the meaning and themes in text collections, and apply advanced analytics to text, like optimization and forecasting. Contextual extraction. Automatically pull structured information from text-based sources. Sentiment analysis. Identifying the mood or subjective opinions within large amounts of text, including average sentiment and opinion mining. Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa. Document summarization. Automatically generating synopses of large bodies of text. Machine translation. Automatic translation of text or speech from one language to another. In all these cases, the overarching goal is to take raw language input and use linguistics and algorithms to transform or enrich the text in such a way that it delivers greater value. || R Language || What is R? R is a programming language developed by Ross Ihaka and Robert Gentleman in 1993. R possesses an extensive catalog of statistical and graphical methods. It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the R libraries are written in R, but for heavy computational tasks, C, C++ and Fortran codes are preferred. R is not only entrusted by academic, but many large companies also use R programming language, including Uber, Google, Airbnb, Facebook and so on. Data analysis with R is done in a series of steps; programming, transforming, discovering, modeling and communicate the results Program: R is a clear and accessible programming tool Transform: R is made up of a collection of libraries designed specifically for data science Discover: Investigate the data, refine your hypothesis and analyze them Model: R provides a wide array of tools to capture the right model for your data Communicate: Integrate codes, graphs, and outputs to a report with R Markdown or build Shiny apps to share with the world What is R used for? Statistical inference Data analysis Machine learning algorithm R package The primary uses of R is and will always be, statistic, visualization, and machine learning. The picture below shows which R package got the most questions in Stack Overflow. In the top 10, most of them are related to the workflow of a data scientist: data preparation and communicate the results. | https://www.udemy.com/course/learn-data-science-deep-learning-machine-learning-nlp-r/#instructor-1 | We are an emerging E-Learning company aiming to teach people advanced concepts starting from scratch. We offer real-time projects through real-world data., which help people to develop skills through on-hand one-to-one training. We are currently in the process of creating more online courses and publishing books. We teach: Data Science Personal Development Programming Finance Digital Marketing Stay Tuned for more | NLP | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Deep Reinforcement Learning 2.0 | The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG | 4.7 | 1050 | 9125 | Created by Hadelin de Ponteves, Ligency I Team, Ligency Team | Nov-22 | English | $12.99 | 9h 38m total length | https://www.udemy.com/course/deep-reinforcement-learning/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1624105 | Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). To approach this model the right way, we structured the course in three parts: Part 1: Fundamentals In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more. Part 2: The Twin-Delayed DDPG Theory We will study in depth the whole theory behind the model. You will clearly see the whole construction and training process of the AI through a series of clear visualization slides. Not only will you learn the theory in details, but also you will shape up a strong intuition of how the AI learns and works. The fundamentals in Part 1, combined to the very detailed theory of Part 2, will make this highly advanced model accessible to you, and you will eventually be one of the very few people who can master this model. Part 3: The Twin-Delayed DDPG Implementation We will implement the model from scratch, step by step, and through interactive sessions, a new feature of this course which will have you practice on many coding exercises while we implement the model. By doing them you will not follow passively the course but very actively, therefore allowing you to effectively improve your skills. And last but not least, we will do the whole implementation on Colaboratory, or Google Colab, which is a totally free and open source AI platform allowing you to code and train some AIs without having any packages to install on your machine. In other words, you can be 100% confident that you press the execute button, the AI will start to train and you will get the videos of the spider and humanoid running in the end. | https://www.udemy.com/course/deep-reinforcement-learning/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Misc | Founder/Entrepreneur | >=4 | Below 10K | Below 10K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
Machine Learning and AI: Support Vector Machines in Python | Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression | 4.9 | 1044 | 23063 | Created by Lazy Programmer Inc. | Nov-22 | English | $12.99 | 8h 53m total length | https://www.udemy.com/course/support-vector-machines-in-python/ | Lazy Programmer Inc. | Artificial intelligence and machine learning engineer | 4.6 | 140635 | 511181 | Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses. These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you’ll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram. The toughest obstacle to overcome when you’re learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability. Not so! In this course, we take a very methodical, step-by-step approach to build up all the theory you need to understand how the SVM really works. We are going to use Logistic Regression as our starting point, which is one of the very first things you learn about as a student of machine learning. So if you want to understand this course, just have a good intuition about Logistic Regression, and by extension have a good understanding of the geometry of lines, planes, and hyperplanes. This course will cover the critical theory behind SVMs: Linear SVM derivation Hinge loss (and its relation to the Cross-Entropy loss) Quadratic programming (and Linear programming review) Slack variables Lagrangian Duality Kernel SVM (nonlinear SVM) Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels Learn how to achieve an infinite-dimensional feature expansion Projected Gradient Descent SMO (Sequential Minimal Optimization) RBF Networks (Radial Basis Function Neural Networks) Support Vector Regression (SVR) Multiclass Classification For those of you who are thinking, "theory is not for me", there’s lots of material in this course for you too! In this course, there will be not just one, but two full sections devoted to just the practical aspects of how to make effective use of the SVM. We’ll do end-to-end examples of real, practical machine learning applications, such as: Image recognition Spam detection Medical diagnosis Regression analysis For more advanced students, there are also plenty of coding exercises where you will get to try different approaches to implementing SVMs. These are implementations that you won't find anywhere else in any other course. Thanks for reading, and I’ll see you in class! "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: Calculus Matrix Arithmetic / Geometry Basic Probability Logistic Regression Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out | https://www.udemy.com/course/support-vector-machines-in-python/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | Machine Learning | Engineer/Developer | >=4 | Below 10K | >=20K | >=4 | >=1 Lakh | >=5 Lakh | |||||||||||||||||
Statistics & Mathematics for Data Science & Data Analytics | Learn the statistics & probability for data science and business analysis | 4.6 | 1043 | 6025 | Created by Nikolai Schuler | Nov-22 | English | $9.99 | 11h 24m total length | https://www.udemy.com/course/statistics-for-data-science-data-analytics/ | Nikolai Schuler | Data Scientist and BI Consultant | 4.6 | 21670 | 115083 | Are you aiming for a career in Data Science or Data Analytics? Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory. Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics. I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course. Why should you take this course? This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data This course is taught by an actual mathematician that is in the same time also working as a data scientist. This course is balancing both: theory & practical real-life example. After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis. What is in this course? This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly. If you feel like this sounds good to you, then take this chance to improve your skills und advance career by enrolling in this course. | https://www.udemy.com/course/statistics-for-data-science-data-analytics/#instructor-1 | Are you thinking about pursuing a career as a Data Analyst or Data Scientist? Do you ever think that your career could take a leap forward if you would have more knowledge and skills in the world of data? Perhaps you are even feeling overwhelmed by the number of courses available or by the fact that your life is already too full to concentrate on one more course? I am Nikolai Schuler, I am a data scientist and BI consultant, and I have been there too... A few years ago I noticed that the world of data benefits from many new tools and technologies. However, I also realized that it is extremely difficult to get trained in the field: Practical courses with real quality content are rare and are often structured in such a way that they are incompatible with a working life full of other tasks and activities. While going through hours of research and training, I came up with the idea of creating a course that would offer extremely valuable content but that would be at the same time easy to follow due to its structure. My goal is to help as many people as possible to pursue their desired career in this new Digital Age by enabling them to upgrade their data analysis skills. I am proud to say that I am heading in the right direction as my courses have already found their audience in over 170 countries and received thousands of positive feedbacks. I am super excited about equipping you with the skillset to master Data Science and Data Analytics! If you are looking for quality AND approachable training, then jump onboard! I am really looking forward to leveling up your IT skills. -- German version -- In meiner Arbeit als Data Scientist in einem großen Konzern sehe ich, dass in der heutigen Zeit neue Tools riesige Vorteile bringen und sie nicht mehr wegzudenken sind. Allerdings sehe ich auch, dass es nicht immer leicht ist, sich neben der täglichen Arbeit in diese neuen Tools einzuarbeiten. Mir selbst ist es auch schwer gefallen Kurse zu finden, die einerseits qualitativ hochwertig und andererseits verständlich und gut strukturiert sind. Aus diesem Grund habe ich mich dazu entschlossen, selbst strukturierte und praxisorientierte Kurse zu erstellen. Da ich sowohl für einfache Anwender als auch für Datenspezialisten Einweisungen in Power BI und anderen Tools gebe, bin ich einerseits technischer Spezialist, habe aber andererseits auch ein gutes Gefühl für die, die nicht die reinen Datenspezialisten sind. Mir zu überlegen, wie man das Wissen wirklich sinnvoll und verständlich vermitteln kann, ist das, was mir dabei Freude macht. | Data Analyst | Consultant | Yes | >=4 | Below 10K | Below 10K | >=4 | Below 1 Lakh | >=1 Lakh | ||||||||||||||||
Machine Learning for Absolute Beginners - Level 1 | Learn the Fundamental Concepts of Artificial Intelligence and Machine Learning as the Next Game-Changing Technology | 4.4 | 1033 | 23931 | Created by Idan Gabrieli | Sep-22 | English | $9.99 | 2h 9m total length | https://www.udemy.com/course/machine-learning-for-absolute-beginners-level-1/ | Idan Gabrieli | Online Teacher | Data, Cloud, AI | 4.5 | 6697 | 137726 | ***** Feedback from Students ******** "Good course for anyone who wants to make some sense of all the proper terminology and basic methodology of AI. Idan's explanations are very clear and to the point, no fluff and no distractions!" Grace H. "The course was actually amazing, giving me much more insight into AI. " Patrick A "best ML course ever. " Parmanand S. "It was a great experience." Dharini S. "Indeed a good beginner's course which I actually wanted". Sharon A. "Good and simple enough to start learning ML." Cogent Systems. "Overall, a very good course to understand the basic concepts of machine learning." Arshad. ************************************** Machine Learning The concept of Artificial Intelligence is used in sci-fiction movies to describe a virtual entity that crossed some critical threshold point and developed self-awareness. And like any good Hollywood movie, this entity will turn against humankind. OMG! It’s a great concept to fuel our basic survival fear; otherwise, no one will buy a ticket to the next Terminator movie 😉 As you may guess, things, in reality, are completely different. Artificial Intelligence is one of the biggest revolutions in the software industry. It is a mind-shift on how to develop software applications. Instead of using hard-coded rules for performing something, we let the machines learn things from data, decipher the complex patterns automatically, and then use it for multiple use cases. AI-Powered Applications There are growing amounts of AI-powered applications in a variety of practical use cases. Web sites are using AI to better recommend visitors about products and services. The ability to recognize objects in real-time video streams is driven by machine learning. It is a game-changing technology, and the game just started. Simplifying Things The concept of AI and ML can be a little bit intimidating for beginners, and specifically for people without a substantial background in complex math and programming. This training is a soft starting point to walk you through the fundamental theoretical concepts. We are going to open the mysterious AI/ML black-box, and take a look inside, get more familiar with the terms being used in the industry. It is going to be a super interesting story. It is important to mention that there are no specific prerequisites for starting this training, and it is designed for absolute beginners. Would you like to join the upcoming Machine Learning revolution? | https://www.udemy.com/course/machine-learning-for-absolute-beginners-level-1/#instructor-1 | For the past decade, Idan Gabrieli has been working in various engineering positions at the heart of Israel's high-tech industry, also called the start-up nation. Through his career, Idan has gained extensive experience working with hundreds of business companies, helping them transform challenges and opportunities into practical use cases while leveraging cutting-edge technologies. Idan has comprehensive knowledge that spans multiple domains, including cloud computing, machine learning, data science, electronics, and more. In 2014, Idan started to create and publish online courses on various topics while teaching thousands of students worldwide. In 2021-2022, Idan was recognized as a top-seller and high-rated instructor in multiple leading educational providers. As part of his teaching style, Idan is well-known for simplifying complex technology topics and providing high-quality educational content suitable to the relevant audience. Every course has specific learning objectives, easy-to-follow structure, and straight-to-point material while combining various multimedia teaching options. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 10K | >=20K | >=4 | Below 10 K | >=1 Lakh | |||||||||||||||||
Deep Learning :Adv. Computer Vision (object detection+more!) | Transfer Learning, TensorFlow Object detection, Classification, Yolo object detection, real time projects much more..!! | 4.1 | 1017 | 23899 | Created by Jay Bhatt | May-21 | English | $10.99 | 7h 23m total length | https://www.udemy.com/course/advanced-computer-vision-transfer-learning-with-tensorflow/ | Jay Bhatt | Data Scientist by Profession Instructor by Passion | 3.8 | 3446 | 28533 | Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Here is the details about the project. Here we will star from colab understating because that will help to use free GPU provided by google to train up our model. We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception. We will understand object detection modules in detail using both tensorflow object detection api as well as YOLO algorithms. We’ll be looking at a state-of-the-art algorithm called RESNET and MobileNetV2 which is both faster and more accurate than its predecessors. One best thing is you will understand the core basics of CNN and how it converts to object detection slowly. I hope you’re excited to learn about these advanced applications of CNNs Yolo and Tensorflow, I’ll see you in class! AMAGING FACTS: · This course give’s you full hand’s on experience of training models in colab GPU. · Instead of focusing on the detailed inner workings of CNNs (which we've already done), we'll focus on high-level building blocks. The result? Almost zero math. · Another result? No complicated low-level code such as that written in Tensorflow, Theano,YOLO, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you. Suggested Prerequisites: · Know how to build, train, and use a CNN using some library (preferably in Python) · Understand basic theoretical concepts behind convolution and neural networks · Decent Python coding skills, preferably in data science and the Numpy Stack Who this course is for: · Students and professionals who want to take their knowledge of computer vision and deep learning to the next level · Anyone who wants to learn about object detection algorithms like SSD and YOLO · Anyone who wants to learn how to write code for neural style transfer · Anyone who wants to use transfer learning · Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast · Anyone who is starting with computer vison | https://www.udemy.com/course/advanced-computer-vision-transfer-learning-with-tensorflow/#instructor-1 | Hi my name is Jay Having 5 years of experience in a leading Data Science Company, I have completed my masters degree adv mathematics and FEM . I love making educational videos and content. check out my you-tube channel and all udamy tutorial and stay updated with new techniques of data science and machine learning. Hope you will enjoy this lovely journey of Data science and machine learning. | Computer Vision | Data Scientist | >=4 | Below 10K | >=20K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Natural Language Processing: NLP With Transformers in Python | Learn next-generation NLP with transformers for sentiment analysis, Q&A, similarity search, NER, and more | 4.4 | 1009 | 18703 | Created by James Briggs | Aug-22 | English | $9.99 | 11h 30m total length | https://www.udemy.com/course/nlp-with-transformers/ | James Briggs | ML Engineer | 4.4 | 1133 | 20017 | Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again. In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR. We cover several key NLP frameworks including: HuggingFace's Transformers TensorFlow 2 PyTorch spaCy NLTK Flair And learn how to apply transformers to some of the most popular NLP use-cases: Language classification/sentiment analysis Named entity recognition (NER) Question and Answering Similarity/comparative learning Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application. All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as: History of NLP and where transformers come from Common preprocessing techniques for NLP The theory behind transformers How to fine-tune transformers We cover all this and more, I look forward to seeing you in the course! | https://www.udemy.com/course/nlp-with-transformers/#instructor-1 | An ML engineer with experience working with Silicon Valley startups, the big four accountancy firms, and other leading financial institutions. Since entering the world of data science and machine learning, James has specialized in natural language, working on many successful, production-level NLP projects with industry-standard technologies. Aside from his wide-ranging industry experience, James is a prolific writer and content creator - with the goal of sharing the fascinating world of machine learning (and in particular NLP) with all those that listen. James' articles alone have gathered more than two million viewers. Coming from a self-taught background, James understands the difficult and winding path towards becoming a data scientist or machine learning engineer. His goal is to deliver content that illuminates that path for others and helps them on their own journey. | NLP | Engineer/Developer | >=4 | Below 10K | >=15K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Learn BERT - most powerful NLP algorithm by Google | Understand and apply Google's game-changing NLP algorithm to real-world tasks. Build 2 NLP applications. | 3.9 | 1008 | 6235 | Created by Martin Jocqueviel, Ligency I Team, Ligency Team | Apr-22 | English | $12.99 | 5h 29m total length | https://www.udemy.com/course/bert-nlp-algorithm/ | Martin Jocqueviel | Freelance data scientist | 4.2 | 3100 | 55396 | Dive deep into the BERT intuition and applications: Suitable for everyone: We will dive into the history of BERT from its origins, detailing any concept so that anyone can follow and finish the course mastering this state-of-the-art NLP algorithm even if you are new to the subject. Powerful and disruptive: Learn the concepts behind a new BERT, getting rid of RNNs, CNNs and other heavy deep learning models to implement a more intuitive way to process language that will suit a wide range of NLP purposes, including yours! User-friendly and efficient: We’ve designed the course using the latest technologies, using Tensorflow 2.0 and Google Colab, assuring that you won’t have any local machine/software version/compatibility issues and that you are using the most up-to-date tools. | https://www.udemy.com/course/bert-nlp-algorithm/#instructor-1 | After graduating in Physics and Mathematics from École Polytechnique in France, I specialized in Machine Learning and Artificial Intelligence at ENS. As a Mathematician I like to grasp the full implications behind every algorithm, while as a physicist I want to consider the reality of data from a practical point of view when building an AI. I decided to combined those two aspects of science to build inspiring, intuitive and useful courses for everyone! | NLP | Data Scientist | >=3 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Regression Analysis / Data Analytics in Regression | Gain Important and Highly Marketable Skills in Regression Analysis - Tame the Regression Beast Today! | 4.6 | 1008 | 5518 | Created by Quantitative Specialists | Nov-17 | English | $9.99 | 2h 9m total length | https://www.udemy.com/course/regression-statistics/ | Quantitative Specialists | Specializing in Statistics, Research Design, and Measurement | 4.6 | 5017 | 23575 | November, 2019. Get marketable and highly sought after skills in this course while substantially increasing your knowledge of data analytics in regression. All course videos created and narrated by an award winning instructor and textbook author of quantitative methods. This course covers running and evaluating linear regression models (simple regression, multiple regression, and hierarchical regression), including assessing the overall quality of models and interpreting individual predictors for significance. R-Square is explored in depth, including how to interpret R-Square for significance. Together with coverage of simple, multiple and hierarchical regression, we'll also explore correlation, an important statistical procedure that is closely related to regression. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Examples of running regression in both SPSS and Excel programs provided. Lectures provided in high quality, HD video with course quizzes available to help cement the concepts. Taught by a PhD award-winning university instructor with over 15 years of teaching experience. At Quantitative Specialists, our highest priority is in creating crystal-clear, accurate, easy-to-follow videos. Tame the regression beast once and for all – enroll today! | https://www.udemy.com/course/regression-statistics/#instructor-1 | Quantitative Specialists (QS) was founded by an award-winning university instructor who has taught statistics courses for over 15 years. At QS, we are passionate about all things statistical, especially in helping others understand this often-feared subject matter. Our focus is in helping you to succeed in all your statistics work! | Data Analyst | >=4 | Below 10K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Learn Machine learning & AI (Including Hands-on 3 Projects) | Get well versed with Machine learning and AI by working on Hands-on Projects. | 4 | 1005 | 248343 | Created by EdYoda Digital University | Sep-20 | English | $9.99 | 1h 57m total length | https://www.udemy.com/course/machine-learning-and-ai-with-hands-on-projects/ | EdYoda Digital University | Visit us at www.edyoda.com | 4.2 | 37879 | 1018982 | Do you feel overwhelmed going through all the AI and Machine learning study materials? These Machine learning and AI projects will get you started with the implementation of a few very interesting projects from scratch. The first one, a Web application for Object Identification will teach you to deploy a simple machine learning application. The second one, Dog Breed Prediction will help you building & optimizing a model for dog breed prediction among 120 breeds of dogs. This is built using Deep Learning libraries. Lastly, Credit Card Fraud detection is one of the most commonly used applications in the Finance Industry. We talk about it from development to deployment. Each of these projects will help you to learn practically. Who's teaching you in this course? I am Professional Trainer and consultant for Languages C, C++, Python, Java, Scala, Big Data Technologies - PySpark, Spark using Scala Machine Learning & Deep Learning- sci-kit-learn, TensorFlow, TFLearn, Keras, h2o and delivered at corporates like GE, SCIO Health Analytics, Impetus, IBM Bangalore & Hyderabad, Redbus, Schnider, JP Morgan - Singapore & HongKong, CISCO, Flipkart, MindTree, DataGenic, CTS - Chennai, HappiestMinds, Mphasis, Hexaware, Kabbage. I have shared my knowledge that will guide you to understand the holistic approach towards ML. Here are a few reasons for you to pursue a career in Machine Learning: 1) Machine learning is a skill of the future – Despite the exponential growth in Machine Learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in Machine Learning, you will have a secure career in a technology that is on the rise. 2) Work on real challenges – Businesses in this digital age face a lot of issues that Machine learning promises to solve. As a Machine Learning Engineer, you will work on real-life challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives high satisfaction. 3) Learn and grow – Since Machine Learning is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer. 4) An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have the experience and expertise to follow an upward career graph and approach your ideal employers. 5) Build a lucrative career– The average salary of a Machine Learning engineer is one of the top reasons why Machine Learning seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by. 6) Side-step into data science – Machine learning skills help you expand avenues in your career. Machine Learning skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge. Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise. Practical Learning !! Project-based learning has proven to be one of the most effective ways to engage students and provide a practical application for what they’re learning and it provides opportunities for students to collaborate or drive their learning, but it also teaches them skills such as problem-solving and helps to develop additional skills integral to their future, such as critical thinking and time management By pursuing this course you will able to understand the concept of Machine learning at the next level you will also get to know about Artificial intelligence and that will boost your skill set to be a successful ML engineer. Enroll now, see you in class!! Happy learning! Team Edyoda | https://www.udemy.com/course/machine-learning-and-ai-with-hands-on-projects/#instructor-1 | EdYoda is re-imagining skill based education, educating on job-relevant real world skills. Edyoda courses are on job-relevant technical skills. We have professional team of instructors, some of the courses we specialize in are Web development, Mobile App Development, Cloud & DevOps, Machine Learning, Artificial Intelligence and Big Data. We believe that access to education and opportunities is the biggest enabler and we are on a mission to enable the same for everyone across the world. | Machine Learning | >=4 | Below 10K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Train YOLO for Object Detection with Custom Data | Build your own detector by labelling, training and testing on image, video and in real time with camera: YOLO v3 and v4 | Bestseller | 4.5 | 994 | 5044 | Created by Valentyn Sichkar | Jun-22 | English | $10.99 | 7h 6m total length | https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/ | Valentyn Sichkar | Computer Vision, Machine Learning, Image Processing | 4.4 | 1045 | 5424 | In this hands-on course, you'll train your own Object Detector using YOLO v3-v4 algorithms. As for beginning, you’ll implement already trained YOLO v3-v4 on COCO dataset. You’ll detect objects on image, video and in real time by OpenCV deep learning library. The code templates you can integrate later in your own future projects and use them for your own trained YOLO detectors. After that, you’ll label individual dataset as well as create custom one by extracting needed images from huge existing dataset. Next, you’ll convert Traffic Signs dataset into YOLO format. Code templates for converting you can modify and apply for other datasets in your future work. When datasets are ready, you’ll train and test YOLO v3-v4 detectors in Darknet framework. As for Bonus part, you’ll build graphical user interface for Object Detection by YOLO and by the help of PyQt. This project you can represent as your results to your supervisor or to make a presentation in front of classmates or even mention it in your resume. Content Organization. Each Section of the course contains: Video Lectures Coding Activities Code Templates Quizzes Downloadable Instructions Discussion Opportunities Video Lectures of the course have SMART objectives: S - specific (the lecture has specific objectives) M - measurable (results are reasonable and can be quantified) A - attainable (the lecture has clear steps to achieve the objectives) R - result-oriented (results can be obtained by the end of the lecture) T - time-oriented (results can be obtained within the visible time frame) | https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/#instructor-1 | I am PhD student in Intelligent Systems. Studying Computer Vision, Machine Learning, Image Processing. Developing algorithms for safety autonomous vehicles. I have a BSc in Manufacturing automation where I obtained knowledge on how to improve production speed and quality by integrating more efficient equipment, like non-stop filtering, velocity and temperature control in real time, as well as optical sensors for sorting and classifying different types of products. And I have an MSc in Intelligent Systems where I obtained extensive knowledge of machine learning, computer vision, and intelligent robotics. My final project was to develop Alarm-Warning system for mobile robot that has information about distances to the objects - Safe distance, Warning distance and Alarm distance. The system creates a kind of bubble around mobile robot with green, yellow and red zones preventing collisions with obstacles. I have published research on using different dimensions of filters for convolutional neural networks (ConvNet) for effective classification of Traffic Signs. Trained ConvNet I deployed on the Web on Linux VPS and on the basis of Flask framework in order to have opportunity to test classification online. Professional interests: Computer Vision, Convolutional Neural Networks, Autopilot Car's System, Autonomous Robots. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
[2022] Machine Learning and Deep Learning Bootcamp in Python | Machine Learning, Neural Networks, Computer Vision, Deep Learning and Reinforcement Learning in Keras and TensorFlow | 4.4 | 988 | 10781 | Created by Holczer Balazs | Sep-22 | English | $13.99 | 32h 37m total length | https://www.udemy.com/course/introduction-to-machine-learning-in-python/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | Interested in Machine Learning, Deep Learning and Computer Vision? Then this course is for you! This course is about the fundamental concepts of machine learning, deep learning, reinforcement learning and machine learning. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together. We will use Python with SkLearn, Keras and TensorFlow. ### MACHINE LEARNING ### 1.) Linear Regression understanding linear regression model correlation and covariance matrix linear relationships between random variables gradient descent and design matrix approaches 2.) Logistic Regression understanding logistic regression classification algorithms basics maximum likelihood function and estimation 3.) K-Nearest Neighbors Classifier what is k-nearest neighbour classifier? non-parametric machine learning algorithms 4.) Naive Bayes Algorithm what is the naive Bayes algorithm? classification based on probability cross-validation overfitting and underfitting 5.) Support Vector Machines (SVMs) support vector machines (SVMs) and support vector classifiers (SVCs) maximum margin classifier kernel trick 6.) Decision Trees and Random Forests decision tree classifier random forest classifier combining weak learners 7.) Bagging and Boosting what is bagging and boosting? AdaBoost algorithm combining weak learners (wisdom of crowds) 8.) Clustering Algorithms what are clustering algorithms? k-means clustering and the elbow method DBSCAN algorithm hierarchical clustering market segmentation analysis ### NEURAL NETWORKS AND DEEP LEARNING ### 9.) Feed-Forward Neural Networks single layer perceptron model feed.forward neural networks activation functions backpropagation algorithm 10.) Deep Neural Networks what are deep neural networks? ReLU activation functions and the vanishing gradient problem training deep neural networks loss functions (cost functions) 11.) Convolutional Neural Networks (CNNs) what are convolutional neural networks? feature selection with kernels feature detectors pooling and flattening 12.) Recurrent Neural Networks (RNNs) what are recurrent neural networks? training recurrent neural networks exploding gradients problem LSTM and GRUs time series analysis with LSTM networks Numerical Optimization (in Machine Learning) gradient descent algorithm stochastic gradient descent theory and implementation ADAGrad and RMSProp algorithms ADAM optimizer explained ADAM algorithm implementation 13.) Reinforcement Learning Markov Decision Processes (MDPs) value iteration and policy iteration exploration vs exploitation problem multi-armed bandits problem Q learning and deep Q learning learning tic tac toe with Q learning and deep Q learning ### COMPUTER VISION ### 14.) Image Processing Fundamentals: computer vision theory what are pixel intensity values convolution and kernels (filters) blur kernel sharpen kernel edge detection in computer vision (edge detection kernel) 15.) Serf-Driving Cars and Lane Detection how to use computer vision approaches in lane detection Canny's algorithm how to use Hough transform to find lines based on pixel intensities 16.) Face Detection with Viola-Jones Algorithm: Viola-Jones approach in computer vision what is sliding-windows approach detecting faces in images and in videos 17.) Histogram of Oriented Gradients (HOG) Algorithm how to outperform Viola-Jones algorithm with better approaches how to detects gradients and edges in an image constructing histograms of oriented gradients using support vector machines (SVMs) as underlying machine learning algorithms 18.) Convolution Neural Networks (CNNs) Based Approaches what is the problem with sliding-windows approach region proposals and selective search algorithms region based convolutional neural networks (C-RNNs) fast C-RNNs faster C-RNNs 19.) You Only Look Once (YOLO) Object Detection Algorithm what is the YOLO approach? constructing bounding boxes how to detect objects in an image with a single look? intersection of union (IOU) algorithm how to keep the most relevant bounding box with non-max suppression? 20.) Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD what is the main idea behind SSD algorithm constructing anchor boxes VGG16 and MobileNet architectures implementing SSD with real-time videos You will get lifetime access to 150+ lectures plus slides and source codes for the lectures! This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back. So what are you waiting for? Learn Machine Learning, Deep Learning and Computer Vision in a way that will advance your career and increase your knowledge, all in a fun and practical way! Thanks for joining the course, let's get started! | https://www.udemy.com/course/introduction-to-machine-learning-in-python/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Machine Learning | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Introduction to AI, Machine Learning and Data Science | Introductory Course for Beginners to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised ML | 4.1 | 988 | 28917 | Created by Pradeep D | May-20 | English | $9.99 | 32m total length | https://www.udemy.com/course/introduction-to-ai-machine-learning-and-data-science/ | Pradeep D | Programmer, Data Engineer and Instructor | 4.1 | 1741 | 56625 | Lets learn basics to transform your career. I promise not to exhaust you with huge number of videos. Artificial Intelligence, Machine Learning, Data Science are the most hot skills in the markets which has potential to help you earn highest salary. These skills has potential to turn your financial to better level which can provide you growth and prosperity. Welcome to the most comprehensive Introduction to AI, Machine Learning and Data Science course! An excellent choice for beginners and professionals looking to expand their knowledge on Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised Learning. This is an introductory course for beginners to boost your knowledge. This course gives introduction to to AI, Machine Learning, Data Science, Deep Learning, Supervised and Unsupervised learning with real time examples where machine learning can be applied to solve or simplify real world business problems. What you'll learn Introduction to buzz words like AI, Machine Learning, Data Science and Deep Learning etc. Real time examples where Machine Learning can be used to solve real world business problems Introduction to Supervised Learning and Unsupervised Learning Introduction to Natural Language Processing Why python is popular for Machine Learning Prerequisite: You just need computer or mobile phone with internet connection to access course material. No prerequisites ! Happy Learning! | https://www.udemy.com/course/introduction-to-ai-machine-learning-and-data-science/#instructor-1 | Hello, I'm Pradeep. Extensively worked on unix platform to design and develop end to end framework for an enterprises to drive the data manipulation pipelines. I am data professional proficient in Data Engineering techniques including Data Science, Machine learning and Deep Learning. Solved several business problems end to end. Total more than 8 years of experience. All the best for your journey of learning further. | Machine Learning | Engineer/Developer | >=4 | Below 1K | >=25K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
2022 Python Data Analysis & Visualization Masterclass | Pandas, Matplotlib, Seaborn, & More! Analyze Dozens of Datasets & Create Stunning Visualizations | 4.7 | 978 | 10268 | Created by Colt Steele | Feb-22 | English | $9.99 | 20h 29m total length | https://www.udemy.com/course/python-data-analysis-visualization/ | Colt Steele | Developer and Bootcamp Instructor | 4.7 | 430784 | 1383683 | Welcome to (what I think is) the web's best course on Pandas, Matplotlib, Seaborn, and more! This course will level up your data skills to help you grow your career in Data Science, Machine Learning, Finance, Web Development, or any tech-adjacent field. This is a tightly structured course that covers a ton, but it's all broken down into human-sized pieces rather than an overwhelming reference manual that throws everything at you at once. After each and every new topic, you'll have the chance to practice what you're learning and challenge yourself with exercises and projects. We work with dozens of fun and real-world datasets including Amazon bestsellers, Rivian stock prices, Presidential Tweets, Bitcoin historic data, and UFO sightings. If you're still reading, let me tell you a little about the curriculum.. In the course, you'll learn how to: Work with Jupyter Notebooks Use Pandas to read and manipulate datasets Work with DataFrames and Series objects Organize, filter, clean, aggregate, and analyze DataFrames Extract and manipulate date, time, and textual information from data Master Hierarchical Indexing Merge datasets together in Pandas Create complex visualizations with Matplotlib Use Seaborn to craft stunning and meaningful visualizations Create line, bar, box, scatter, pie, violin, rug, swarm, strip, and other plots! What makes this course different from other courses on the same topics? First and foremost, this course integrates visualizations as soon as possible rather than tacking it on at the end, as many other courses do. You'll be creating your first plots within the first couple of sections! Additionally, we start using real datasets from the get go, unlike most other courses which spend hours working with dull, fake data (colors, animals, etc) before you ever see your first real dataset. With all of that said, I feel bad trash talking my competitors, as there are quite a few great courses on the platform 🙂 I think that about wraps it up! The topics in this courses are extremely visual and immediate, which makes them a joy to teach (and hopefully for you to learn). If you have even a passing interest in these topics, you'll likely enjoy the course and tear through it quickly. This stuff might seem intimidating, but it's actually really approachable and fun! I'm not kidding when I say this is my favorite course I've ever made. I hope you enjoy it too. | https://www.udemy.com/course/python-data-analysis-visualization/#instructor-1 | Hi! I'm Colt. I'm a developer with a serious love for teaching. I've spent the last few years teaching people to program at 2 different immersive bootcamps where I've helped hundreds of people become web developers and change their lives. My graduates work at companies like Google, Salesforce, and Square. Most recently, I led Galvanize's SF's 6 month immersive program as Lead Instructor and Curriculum Director. After graduating from my class, 94% of my students went on to receive full-time developer roles. I also worked at Udacity as a Senior Course Developer on the web development team where I got to reach thousands of students daily. I’ve since focused my time on bringing my classroom teaching experience to an online environment. In 2016 I launched my Web Developer Bootcamp course, which has since gone on to become one of the best selling and top rated courses on Udemy. I was also voted Udemy’s Best New Instructor of 2016. I've spent years figuring out the "formula" to teaching technical skills in a classroom environment, and I'm really excited to finally share my expertise with you. I can confidently say that my online courses are without a doubt the most comprehensive ones on the market. Join me on this crazy adventure! | Python | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | >=4 Lakh | >=10 Lakh | |||||||||||||||||
Want to be a Big Data Scientist? | Should you pursue a career in Data Science? Data Science basics, process, team, roles, skills, transition, opportunities | 4.7 | 969 | 12536 | Created by V2 Maestros, LLC | Mar-18 | English | $9.99 | 1h 25m total length | https://www.udemy.com/course/what-to-be-a-big-data-scientist/ | V2 Maestros, LLC | Big Data / Data Science Experts | 50K+ students | 4.2 | 4162 | 76176 | "Data Science is the sexiest job of the 21st century - It has exciting work and incredible pay". You have been hearing about this a lot. You try to get more information on this and start querying and browsing. You get so many definitions, requirements, predictions, opinions and recommendations. At the end, you are perplexed. And you ask - "What exactly is this field all about? Is it a good career option for me?" **** Please note: This is a career advice course, not a technology course. Data Science has been growing exponentially in the last 5 years. It is also a hybrid field that requires multiple skills and training. We have been training students in Data Science. A number of them committed to training without realizing what it really is. Some were happy, some adjusted their expectations and some regretted committing too soon. We felt that professionals thinking of getting into Data Science needed a primer in what this field is all about. Hence, we came up with this course. Through this course, you will learn about Data Science goals and conceptsProcess FlowRoles and ResponsibilitiesWhere you will fit in to a Data Science team.Building a transition plan Getting into the Data Science field involves significant investment of time. Learn about the field in order to make an informed decision. | https://www.udemy.com/course/what-to-be-a-big-data-scientist/#instructor-1 | V2 Maestros is dedicated to teaching big data / data science courses to students all over the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building Big Data and Data Science talent across the world. | Big Data/Data Engineer | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Probability for Statistics and Data Science | Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions | Bestseller | 4.7 | 955 | 12093 | Created by 365 Careers | Jan-20 | English | $9.99 | 3h 40m total length | https://www.udemy.com/course/probability-for-statistics-and-data-science/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | Probability is probably the most fundamental skill you need to acquire if you want to be successful in the world of business. What most people don’t realize is that having a probabilistic mindset is much more important than knowing “absolute truths”. You are already here, so actually you know that. And it doesn’t matter if it is pure probability, statistics, business intelligence, finance or data science where you want to apply your probability knowledge… Probability for Statistics and Data Science has your back! This is the place where you’ll take your career to the next level – that of probability, conditional probability, Bayesian probability, and probability distributions. You may be wondering: “Hey, but what makes this course better than all the rest?” Probability for Statistics and Data Science has been carefully crafted to reflect the most in-demand skills that will enable you to understand and compute complicated probabilistic concepts. This course is: Easy to understand Comprehensive Practical To the point Beautifully animated (with amazing video quality) Packed with plenty of exercises and resources That’s all great, but what will you actually learn? Probability. And nothing less. To be more specific, we focus on the business implementation of probability concepts. This translates into a comprehensive course consisting of: An introductory part that will acquaint you with the most basic concepts in the field of probability: event, sample space, complement, expected value, variance, probability distribution function We gradually build on your knowledge with the first widely applicable formulas: Combinatorics or the realm of permutations, variations, and combinations. That’s the place where you’ll learn the laws that govern “everyday probability” Once you’ve got a solid background, you’ll be ready for some deeper probability theory – Bayesian probability. Have you seen this expression: P(A|B) = P(B|A)P(A)/P(B) ? That’s the Bayes’ theorem – the most fundamental building block of Bayesian inference. It seems complicated but it will take you less than 1 hour to understand not only how to read it, but also how to use it and prove it To get there you’ll learn about unions, intersections, mutually exclusive sets, overlapping sets, conditional probability, the addition rule, and the multiplication rule Most of these topics can be found online in one form or another. But we are not bothered by that because we are certain of the outstanding quality of teaching that we provide. What we are really proud of, though, is what comes next in the course. Distributions. Distributions are something like the “heart” of probability applied in data science. You may have heard of many of them, but this is the only place where you’ll find detailed information about many of the most common distributions. Discrete: Uniform distribution, Bernoulli distribution, Binomial distribution (that’s where you’ll see a lot of the combinatorics from the previous parts), Poisson Continuous: Normal distribution, Standard normal distribution, Student’s T, Chi-Squared, Exponential, Logistic Not only do we have a dedicated video for each one of them, how to determine them, where they are applied, but also how to apply their formulas. Finally, we’ll have a short discussion on 3 of the most common places where you can stumble upon probability: Finance Statistics Data Science If that’s not enough, keep in mind that we’ve got real-life cases after each of our sections. We know that nobody wants to learn dry theory without seeing it applied to real business situations so that’s in store, too! We think that this will be enough to convince you curriculum-wise. But we also know that you really care about WHO is teaching you, too. Teaching is our passion We worked hard for over four months to create the best possible Probability course that would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, are just some of the perks you will get. What else? Exceptional Q&A support. Yes. That’s our favorite part – interacting with you on the various topics you learn about (and you are going to love it, too!) What makes this course different from the rest of the Probability courses out there? High-quality production – HD video and animations (This isn’t a collection of boring lectures!) Knowledgeable instructor (an adept mathematician who has competed at an international level) who will bring you not only his probability knowledge but the complicated interconnections between his areas of expertise – finance and data science Comprehensive – we will cover all major probability topics and skills you need to level up your career Extensive Case Studies - helping you reinforce everything you’ve learned Exceptional support – we said that, but let’s say it again - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day Succinct – the biggest investment you’ll make is your own time. And we will not waste it. All our teaching is straight to the point Still not convinced? Here’s why you need these skills? Salary/Income – most businesses are starting to realize the advantages of implementing data-driven decisions. And those are all stepping on probability. A probabilistic mindset is definitely one of the non-automatable skills that managers of the next decade will be expected to have Promotions and secure future – If you understand probability well, you will be able to back up your business and positions in much more convincing way, draining from quantitative evidence; needless to say, that’s the path to career growth New horizons – probability is a pathway to many positions in any industry. While it is rarely a full-time position, it is crucial for most business jobs nowadays. And it’s not a boring aspect! Please bear in mind that the course comes with Udemy’s 30-day money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you. Let's start learning together now! | https://www.udemy.com/course/probability-for-statistics-and-data-science/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Statistics | Yes | >=4 | Below 1K | >=10K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||
Artificial Intelligence (ARS): Build the Most Powerful AI | Learn, build and implement the most powerful AI model at home. Compete with multi-billion dollars companies using ARS. | 4.3 | 937 | 9220 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Ligency Team | Nov-22 | English | $13.99 | 4h 52m total length | https://www.udemy.com/course/artificial-intelligence-ars/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 295920 | 1624105 | Two months ago we discovered that a very new kind of AI was invented. The kind of AI which is based on a genius idea and that you can build from scratch and without the need for any framework. We checked that out, we built it, and... the results are absolutely insane! This game-changing AI called Augmented Random Search, ARS for short. And in a very simple implementation, it is able to do an exact same thing that Google Deep Mind did in their accomplishment last year - which is to train an AI to walk and run across a field. However, ARS is 100x times faster and 100x times more powerful. Be prepared for the most significant tech challenges of the 21st century No need for sophisticated algorithms and frameworks What Facebook or Google spent on millions or even more - you can literally do at home! You will be able to compete with multi-billion dollars companies Change the world on your own within months or even weeks Build the most powerful AI that anyone has ever built Get your hands on Artificial Intelligence (ARS): Build the Most Powerful AI You will learn, build and implement the most powerful AI model at home. Compete with multi-billion dollars companies using ARS. | https://www.udemy.com/course/artificial-intelligence-ars/#instructor-1 | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Artificial Intelligence | Founder/Entrepreneur | >=4 | Below 1K | Below 10K | >=4 | >=2.5 Lakh | >=10 Lakh | |||||||||||||||||
Architecting Big Data Solutions | How to architect big data solutions by assembling various big data technologies - modules and best practices | 3.8 | 935 | 5045 | Created by V2 Maestros, LLC | Jan-17 | English | $10.99 | 5h 24m total length | https://www.udemy.com/course/architecting-big-data-solutions/ | V2 Maestros, LLC | Big Data / Data Science Experts | 50K+ students | 4.2 | 4162 | 76176 | The Big Data phenomenon is sweeping across the IT landscape. New technologies are born, new ways of analyzing data are created and new business revenue streams are discovered every day. If you are in the IT field, Big data should already be impacting you in some way. Building Big Data solutions is radically different from how traditional software solutions were built. You cannot take what you learnt in the traditional data solutions world and apply them verbatim to Big Data solutions. You need to understand the unique problem characteristics that drive Big Data and also become familiar with the unending technology options available to solve them. This course will show you how Big Data solutions are built by stitching together big data technologies. It explains the modules in a Big Data pipeline, options available for each module and the Advantages, short comings and use cases for each option. This course is great interview preparation resource for Big Data ! Any one - fresher or experienced should take this course. Note: This is a theory course. There is no source code/ programming included. | https://www.udemy.com/course/architecting-big-data-solutions/#instructor-1 | V2 Maestros is dedicated to teaching big data / data science courses to students all over the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building Big Data and Data Science talent across the world. | Big Data/Data Engineer | >=3 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Master Apache Spark - Hands On! | Learn how to slice and dice data using the next generation big data platform - Apache Spark! | 4.6 | 934 | 5998 | Created by Imtiaz Ahmad, Job Ready Programmer Inc. | Nov-21 | English | $9.99 | 6h 56m total length | https://www.udemy.com/course/the-ultimate-apache-spark-with-java-course-hands-on/ | Imtiaz Ahmad | Senior Software Engineer & Trainer @ Job Ready Programmer | 4.6 | 104261 | 395404 | LAST UPDATED: November 2020 Apache Spark is the next generation batch and stream processing engine. It's been proven to be almost 100 times faster than Hadoop and much much easier to develop distributed big data applications with. It's demand has sky rocketed in recent years and having this technology on your resume is truly a game changer. Over 3000 companies are using Spark in production right now and the list is growing very quickly! Some of the big names include: Oracle, Hortonworks, Cisco, Verizon, Visa, Microsoft, Amazon as well as most of the big world banks and financial institutions! In this course you'll learn everything you need to know about using Apache Spark in your organization while using their latest and greatest Java Datasets API. Below are some of the things you'll learn: How to develop Spark Java Applications using Spark SQL Dataframes Understand how the Spark Standalone cluster works behind the scenes How to use various transformations to slice and dice your data in Spark Java How to marshall/unmarshall Java domain objects (pojos) while working with Spark Datasets Master joins, filters, aggregations and ingest data of various sizes and file formats (txt, csv, Json etc.) Analyze over 18 million real-world comments on Reddit to find the most trending words used Develop programs using Spark Streaming for streaming stock market index files Stream network sockets and messages queued on a Kafka cluster Learn how to develop the most popular machine learning algorithms using Spark MLlib Covers the most popular algorithms: Linear Regression, Logistic Regression and K-Means Clustering You'll be developing over 15 practical Spark Java applications crunching through real world data and slicing and dicing it in various ways using several data transformation techniques. This course is especially important for people who would like to be hired as a java developer or data engineer because Spark is a hugely sought after skill. We'll even go over how to setup a live cluster and configure Spark Jobs to run on the cloud. You'll also learn about the practical implications of performance tuning and scaling out a cluster to work with big data so you'll definitely be learning a ton in this course. This course has a 30 day money back guarantee. You will have access to all of the code used in this course. | https://www.udemy.com/course/the-ultimate-apache-spark-with-java-course-hands-on/#instructor-1 | Imtiaz is an award winning Udemy instructor who is highly experienced in big data technologies and enterprise software architectures. Imtiaz has spent a considerable amount of time building financial software on Wall St. and worked with companies like S&P, Goldman Sachs, AOL and JP Morgan along with helping various startups solve mission critical software problems. In his 13+ years of experience, Imtiaz has also taught software development in programming languages like Java, C++, Python, PL/SQL, Ruby and Javascript. He's the founder of Job Ready Programmer - an online programming school that prepares students of all backgrounds to become professional job-ready software developers through real-world programming courses. | Spark | Senior Role | >=4 | Below 1K | Below 10K | >=4 | >=1 Lakh | >=3.5 Lakh | |||||||||||||||||
Ultimate Python Bootcamp For Data Science & Machine Learning | Learn How To Code Python For Data Science, ML & Data Analysis, With 100+ Exercises and 4 Real Life Projects ! | 4.2 | 916 | 180534 | Created by Pruthviraja L | Mar-20 | English | $9.99 | 15h 45m total length | https://www.udemy.com/course/data-analysis-with-pandas-a-complete-tutorial/ | Pruthviraja L | Professional Educator, Software Trainer and Author | 4 | 1720 | 237859 | Hi, dear learning aspirants welcome to “Ultimate Python Bootcamp For Data Science & Machine Learning ” from beginner to advanced level. We love programming. Python is one of the most popular programming languages in today’s technical world. Python offers both object-oriented and structural programming features. Hence, we are interested in data analysis with Pandas in this course. This course is for those who are ready to take their data analysis skill to the next higher level with the Python data analysis toolkit, i.e. "Pandas". This tutorial is designed for beginners and intermediates but that doesn't mean that we will not talk about the advanced stuff as well. Our approach of teaching in this tutorial is simple and straightforward, no complications are included to make bored Or lose concentration. In this tutorial, I will be covering all the basic things you'll need to know about the 'Pandas' to become a data analyst or data scientist. We are adopting a hands-on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real-life projects (The projects included are the part of large size research-oriented industry projects). I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts. What you will learn: You will become a specialist in the following things while learning via this course “Data Analysis With Pandas”. You will be able to analyze a large file Build a Solid Foundation in Data Analysis with Python After completing the course you will have professional experience on; Pandas Data Structures: Series, DataFrame and Index Objects Essential Functionalities Data Handling Data Pre-processing Data Wrangling Data Grouping Data Aggregation Pivoting Working With Hierarchical Indexing Converting Data Types Time Series Analysis Advanced Pandas Features and much more with hands-on exercises and practice works. | https://www.udemy.com/course/data-analysis-with-pandas-a-complete-tutorial/#instructor-1 | Hi, I am Pruthviraja L, with more than 7+ years of Training and Teaching experience from Technical Institutes, Teaching is my passion. I've obtained my both PG( M. Tech) in Power Systems Engineering and UG(B. E) in Electrical and Electronics Engineering from V. T. U - Belgaum, Karnataka, India. I'm a Certified Data Analyst. I got certifications from various eLearning centers including Udemy, Intellipaat-Bengaluru, LinkedIn eLearning center, Coursera-IBM, Tableau, etc. I've successfully published and presented 6 + research papers in various 'National & International Journals and Conferences'. I'm a member of various National and International Journals including Elsevier and IEEE. I'm a multi-faceted software professional aspirant with demonstrated capability in deploying analytical and programming methodologies to extract insights for boosting and bolstering user requirements. Adept at conducting statistical analysis and data modeling for transforming raw data into actionable strategies. Proficient in visualizing data to execute projects & set organizations on the path to profitability. 6 + years of teaching experience in engineering institutes with programming skills in Matlab, Python, SAS, R and enthusiasm in developing AI and Machine learning skills motivated me to involve in the dynamic working environment to utilize skills and maximize the profit for the organization. I've written a student-friendly textbook in the electrical engineering field titled 'Elements of Electrical Engineering (ISBN: 9789386768001)' under the publication of 'I.K. International Publishing House Pvt Ltd', New Delhi-110016 India. The book is available in many countries including the USA and UK via Amazon and many other seller portals. The book is now started distributing under Wiley India Pvt. Ltd (ISBN: 9789389583939). | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=1 Lakh | >=4 | Below 10 K | >=2 Lakh | |||||||||||||||||
From 0 to 1: Machine Learning, NLP & Python-Cut to the Chase | A down-to-earth, shy but confident take on machine learning techniques that you can put to work today | 4.1 | 899 | 8722 | Created by Loony Corn | Jan-18 | English | $11.99 | 19h 50m total length | https://www.udemy.com/course/from-0-1-machine-learning/ | Loony Corn | An ex-Google, Stanford and Flipkart team | 4.2 | 26022 | 153496 | Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let’s parse that. The course is down-to-earth : it makes everything as simple as possible - but not simpler The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual : most of the techniques are explained with the help of animations to help you understand better. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall. What's Covered: Machine Learning: Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression. Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff Natural Language Processing with Python: Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means Sentiment Analysis: Why it's useful, Approaches to solving - Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python Mitigating Overfitting with Ensemble Learning: Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests Recommendations: Content based filtering, Collaborative filtering and Association Rules learning Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem A Note on Python: The code-alongs in this class all use Python 2.7. Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. The source code has been provided for both Python 2 and Python 3 wherever possible. | https://www.udemy.com/course/from-0-1-machine-learning/#instructor-1 | Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you'll like them 🙂 | NLP | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||||
Data Engineering using AWS Data Analytics | Build Data Engineering Pipelines on AWS using Data Analytics Services - Glue, EMR, Athena, Kinesis, Lambda, Redshift | Bestseller | 4.5 | 895 | 10796 | Created by Durga Viswanatha Raju Gadiraju, Asasri Manthena, Perraju Vegiraju | Jul-22 | English | $9.99 | 26h 15m total length | https://www.udemy.com/course/data-engineering-using-aws-analytics-services/ | Durga Viswanatha Raju Gadiraju | CEO at ITVersity and CTO at Analytiqs, Inc | 4.4 | 13459 | 261251 | Data Engineering is all about building Data Pipelines to get data from multiple sources into Data Lakes or Data Warehouses and then from Data Lakes or Data Warehouses to downstream systems. As part of this course, I will walk you through how to build Data Engineering Pipelines using AWS Data Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, EMR, Kinesis, and many more. Here are the high-level steps which you will follow as part of the course. Setup Development Environment Getting Started with AWS Storage - All about AWS s3 (Simple Storage Service) User Level Security - Managing Users, Roles, and Policies using IAM Infrastructure - AWS EC2 (Elastic Cloud Compute) Data Ingestion using AWS Lambda Functions Overview of AWS Glue Components Setup Spark History Server for AWS Glue Jobs Deep Dive into AWS Glue Catalog Exploring AWS Glue Job APIs AWS Glue Job Bookmarks Development Life Cycle of Pyspark Getting Started with AWS EMR Deploying Spark Applications using AWS EMR Streaming Pipeline using AWS Kinesis Consuming Data from AWS s3 using boto3 ingested using AWS Kinesis Populating GitHub Data to AWS Dynamodb Overview of Amazon AWS Athena Amazon AWS Athena using AWS CLI Amazon AWS Athena using Python boto3 Getting Started with Amazon AWS Redshift Copy Data from AWS s3 into AWS Redshift Tables Develop Applications using AWS Redshift Cluster AWS Redshift Tables with Distkeys and Sortkeys AWS Redshift Federated Queries and Spectrum Here are the details about what you will be learning as part of this course. We will cover most of the commonly used services with hands-on practice which are available under AWS Data Analytics. Getting Started with AWS As part of this section, you will be going through the details related to getting started with AWS. Introduction - AWS Getting Started Create s3 Bucket Create AWS IAM Group and AWS IAM User to have required access on s3 Bucket and other services Overview of AWS IAM Roles Create and Attach Custom AWS IAM Policy to both AWS IAM Groups as well as Users Configure and Validate AWS CLI to access AWS Services using AWS CLI Commands Storage - All about AWS s3 (Simple Storage Service) AWS s3 is one of the most prominent fully managed AWS services. All IT professionals who would like to work on AWS should be familiar with it. We will get into quite a few common features related to AWS s3 in this section. Getting Started with AWS S3 Setup Data Set locally to upload to AWS s3 Adding AWS S3 Buckets and Managing Objects (files and folders) in AWS s3 buckets Version Control for AWS S3 Buckets Cross-Region Replication for AWS S3 Buckets Overview of AWS S3 Storage Classes Overview of AWS S3 Glacier Managing AWS S3 using AWS CLI Commands Managing Objects in AWS S3 using CLI - Lab User Level Security - Managing Users, Roles, and Policies using IAM Once you start working on AWS, you need to understand the permissions you have as a non-admin user. As part of this section, you will understand the details related to AWS IAM users, groups, roles as well as policies. Creating AWS IAM Users Logging into AWS Management Console using AWS IAM User Validate Programmatic Access to AWS IAM User AWS IAM Identity-based Policies Managing AWS IAM Groups Managing AWS IAM Roles Overview of Custom AWS IAM Policies Managing AWS IAM users, groups, roles as well as policies using AWS CLI Commands Infrastructure - AWS EC2 (Elastic Cloud Compute) Basics AWS EC2 Instances are nothing but virtual machines on AWS. As part of this section, we will go through some of the basics related to AWS EC2 Basics. Getting Started with AWS EC2 Create AWS EC2 Key Pair Launch AWS EC2 Instance Connecting to AWS EC2 Instance AWS EC2 Security Groups Basics AWS EC2 Public and Private IP Addresses AWS EC2 Life Cycle Allocating and Assigning AWS Elastic IP Address Managing AWS EC2 Using AWS CLI Upgrade or Downgrade AWS EC2 Instances Infrastructure - AWS EC2 Advanced In this section, we will continue with AWS EC2 to understand how we can manage EC2 instances using AWS Commands and also how to install additional OS modules leveraging bootstrap scripts. Getting Started with AWS EC2 Understanding AWS EC2 Metadata Querying on AWS EC2 Metadata Fitering on AWS EC2 Metadata Using Bootstrapping Scripts with AWS EC2 Instances to install additional softwares on AWS EC2 instances Create an AWS AMI using AWS EC2 Instances Validate AWS AMI - Lab Data Ingestion using Lambda Functions AWS Lambda functions are nothing but serverless functions. In this section, we will understand how we can develop and deploy Lambda functions using Python as a programming language. We will also see how to maintain a bookmark or checkpoint using s3. Hello World using AWS Lambda Setup Project for local development of AWS Lambda Functions Deploy Project to AWS Lambda console Develop download functionality using requests for AWS Lambda Functions Using 3rd party libraries in AWS Lambda Functions Validating AWS s3 access for local development of AWS Lambda Functions Develop upload functionality to s3 using AWS Lambda Functions Validating AWS Lambda Functions using AWS Lambda Console Run AWS Lambda Functions using AWS Lambda Console Validating files incrementally downloaded using AWS Lambda Functions Reading and Writing Bookmark to s3 using AWS Lambda Functions Maintaining Bookmark on s3 using AWS Lambda Functions Review the incremental upload logic developed using AWS Lambda Functions Deploying AWS Lambda Functions Schedule AWS Lambda Functions using AWS Event Bridge Overview of AWS Glue Components In this section, we will get a broad overview of all important Glue Components such as Glue Crawler, Glue Databases, Glue Tables, etc. We will also understand how to validate Glue tables using AWS Athena. AWS Glue (especially Glue Catalog) is one of the key components in the realm of AWS Data Analytics Services. Introduction - Overview of AWS Glue Components Create AWS Glue Crawler and AWS Glue Catalog Database as well as Table Analyze Data using AWS Athena Creating AWS S3 Bucket and Role to create AWS Glue Catalog Tables using Crawler on the s3 location Create and Run the AWS Glue Job to process data in AWS Glue Catalog Tables Validate using AWS Glue Catalog Table and by running queries using AWS Athena Create and Run AWS Glue Trigger Create AWS Glue Workflow Run AWS Glue Workflow and Validate Setup Spark History Server for AWS Glue Jobs AWS Glue uses Apache Spark under the hood to process the data. It is important we setup Spark History Server for AWS Glue Jobs to troubleshoot any issues. Introduction - Spark History Server for AWS Glue Setup Spark History Server on AWS Clone AWS Glue Samples repository Build AWS Glue Spark UI Container Update AWS IAM Policy Permissions Start AWS Glue Spark UI Container Deep Dive into AWS Glue Catalog AWS Glue has several components, but the most important ones are nothing but AWS Glue Crawlers, Databases as well as Catalog Tables. In this section, we will go through some of the most important and commonly used features of the AWS Glue Catalog. Prerequisites for AWS Glue Catalog Tables Steps for Creating AWS Glue Catalog Tables Download Data Set to use to create AWS Glue Catalog Tables Upload data to s3 to crawl using AWS Glue Crawler to create required AWS Glue Catalog Tables Create AWS Glue Catalog Database - itvghlandingdb Create AWS Glue Catalog Table - ghactivity Running Queries using AWS Athena - ghactivity Crawling Multiple Folders using AWS Glue Crawlers Managing AWS Glue Catalog using AWS CLI Managing AWS Glue Catalog using Python Boto3 Exploring AWS Glue Job APIs Once we deploy AWS Glue jobs, we can manage them using AWS Glue Job APIs. In this section we will get overview of AWS Glue Job APIs to run and manage the jobs. Update AWS IAM Role for AWS Glue Job Generate baseline AWS Glue Job Running baseline AWS Glue Job AWS Glue Script for Partitioning Data Validating using AWS Athena Understanding AWS Glue Job Bookmarks AWS Glue Job Bookmarks can be leveraged to maintain the bookmarks or checkpoints for incremental loads. In this section, we will go through the details related to AWS Glue Job Bookmarks. Introduction to AWS Glue Job Bookmarks Cleaning up the data to run AWS Glue Jobs Overview of AWS Glue CLI and Commands Run AWS Glue Job using AWS Glue Bookmark Validate AWS Glue Bookmark using AWS CLI Add new data to the landing zone to run AWS Glue Jobs using Bookmarks Rerun AWS Glue Job using Bookmark Validate AWS Glue Job Bookmark and Files for Incremental run Recrawl the AWS Glue Catalog Table using AWS CLI Commands Run AWS Athena Queries for Data Validation Development Lifecycle for Pyspark In this section, we will focus on the development of Spark applications using Pyspark. We will use this application later while exploring EMR in detail. Setup Virtual Environment and Install Pyspark Getting Started with Pycharm Passing Run Time Arguments Accessing OS Environment Variables Getting Started with Spark Create Function for Spark Session Setup Sample Data Read data from files Process data using Spark APIs Write data to files Validating Writing Data to Files Productionizing the Code Getting Started with AWS EMR (Elastic Map Reduce) As part of this section, we will understand how to get started with AWS EMR Cluster. We will primarily focus on AWS EMR Web Console. Elastic Map Reduce is one of the key service in AWS Data Analytics Services which provide capability to run applications which process large scale data leveraging distributed computing frameworks such as Spark. Planning for AWS EMR Cluster Create AWS EC2 Key Pair for AWS EMR Cluster Setup AWS EMR Cluster with Apache Spark Understanding Summary of AWS EMR Cluster Review AWS EMR Cluster Application User Interfaces Review AWS EMR Cluster Monitoring Review AWS EMR Cluster Hardware and Cluster Scaling Policy Review AWS EMR Cluster Configurations Review AWS EMR Cluster Events Review AWS EMR Cluster Steps Review AWS EMR Cluster Bootstrap Actions Connecting to AWS EMR Master Node using SSH Disabling Termination Protection for AWS EMR Cluster and Terminating the AWS EMR Cluster Clone and Create a New AWS EMR Cluster Listing AWS S3 Buckets and Objects using AWS CLI on AWS EMR Cluster Listing AWS S3 Buckets and Objects using HDFS CLI on AWS EMR Cluster Managing Files in AWS S3 using HDFS CLI on AWS EMR Cluster Review AWS Glue Catalog Databases and Tables Accessing AWS Glue Catalog Databases and Tables using AWS EMR Cluster Accessing spark-sql CLI of AWS EMR Cluster Accessing pyspark CLI of AWS EMR Cluster Accessing spark-shell CLI of AWS EMR Cluster Create AWS EMR Cluster for Notebooks Deploying Spark Applications using AWS EMR As part of this section, we will understand how we typically deploy Spark Applications using AWS EMR. We will be using the Spark Application we deployed earlier. Deploying Applications using AWS EMR - Introduction Setup AWS EMR Cluster to deploy applications Validate SSH Connectivity to Master node of AWS EMR Cluster Setup Jupyter Notebook Environment on AWS EMR Cluster Create required AWS s3 Bucket for AWS EMR Cluster Upload GHActivity Data to s3 so that we can process using Spark Application deployed on AWS EMR Cluster Validate Application using AWS EMR Compatible Versions of Python and Spark Deploy Spark Application to AWS EMR Master Node Create user space for ec2-user on AWS EMR Cluster Run Spark Application using spark-submit on AWS EMR Master Node Validate Data using Jupyter Notebooks on AWS EMR Cluster Clone and Start Auto Terminated AWS EMR Cluster Delete Data Populated by GHAcitivity Application using AWS EMR Cluster Differences between Spark Client and Cluster Deployment Modes on AWS EMR Cluster Running Spark Application using Cluster Mode on AWS EMR Cluster Overview of Adding Pyspark Application as Step to AWS EMR Cluster Deploy Spark Application to AWS S3 to run using AWS EMR Steps Running Spark Applications as AWS EMR Steps in client mode Running Spark Applications as AWS EMR Steps in cluster mode Validate AWS EMR Step Execution of Spark Application Streaming Data Ingestion Pipeline using AWS Kinesis As part of this section, we will go through details related to the streaming data ingestion pipeline using AWS Kinesis which is a streaming service of AWS Data Analytics Services. We will use AWS Kinesis Firehose Agent and AWS Kinesis Delivery Stream to read the data from log files and ingest it into AWS s3. Building Streaming Pipeline using AWS Kinesis Firehose Agent and Delivery Stream Rotating Logs so that the files are created frequently which will be eventually ingested using AWS Kinesis Firehose Agent and AWS Kinesis Firehose Delivery Stream Set up AWS Kinesis Firehose Agent to get data from logs into AWS Kinesis Delivery Stream. Create AWS Kinesis Firehose Delivery Stream Planning the Pipeline to ingest data into s3 using AWS Kinesis Delivery Stream Create AWS IAM Group and User for Streaming Pipelines using AWS Kinesis Components Granting Permissions to AWS IAM User using Policy for Streaming Pipelines using AWS Kinesis Components Configure AWS Kinesis Firehose Agent to read the data from log files and ingest it into AWS Kinesis Firehose Delivery Stream. Start and Validate AWS Kinesis Firehose Agent Conclusion - Building Simple Steaming Pipeline using AWS Kinesis Firehose Consuming Data from AWS s3 using Python boto3 ingested using AWS Kinesis As data is ingested into AWS S3, we will understand how data can ingested in AWS s3 can be processed using boto3. Customizing AWS s3 folder using AWS Kinesis Delivery Stream Create AWS IAM Policy to read from AWS s3 Bucket Validate AWS s3 access using AWS CLI Setup Python Virtual Environment to explore boto3 Validating access to AWS s3 using Python boto3 Read Content from AWS s3 object Read multiple AWS s3 Objects Get the number of AWS s3 Objects using Marker Get the size of AWS s3 Objects using Marker Populating GitHub Data to AWS Dynamodb As part of this section, we will understand how we can populate data to AWS Dynamodb tables using Python as a programming language. Install required libraries to get GitHub Data to AWS Dynamodb tables. Understanding GitHub APIs Setting up GitHub API Token Understanding GitHub Rate Limit Create New Repository for since Extracting Required Information using Python Processing Data using Python Grant Permissions to create AWS dynamodb tables using boto3 Create AWS Dynamodb Tables AWS Dynamodb CRUD Operations Populate AWS Dynamodb Table AWS Dynamodb Batch Operations Overview of Amazon AWS Athena As part of this section, we will understand how to get started with AWS Athena using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Athena Query Editor. Getting Started with Amazon AWS Athena Quick Recap of AWS Glue Catalog Databases and Tables Access AWS Glue Catalog Databases and Tables using AWS Athena Query Editor Create a Database and Table using AWS Athena Populate Data into Table using AWS Athena Using CTAS to create tables using AWS Athena Overview of Amazon AWS Athena Architecture Amazon AWS Athena Resources and relationship with Hive Create a Partitioned Table using AWS Athena Develop Query for Partitioned Column Insert into Partitioned Tables using AWS Athena Validate Data Partitioning using AWS Athena Drop AWS Athena Tables and Delete Data Files Drop Partitioned Table using AWS Athena Data Partitioning in AWS Athena using CTAS Amazon AWS Athena using AWS CLI As part of this section, we will understand how to interact with AWS Athena using AWS CLI Commands. Amazon AWS Athena using AWS CLI - Introduction Get help and list AWS Athena databases using AWS CLI Managing AWS Athena Workgroups using AWS CLI Run AWS Athena Queries using AWS CLI Get AWS Athena Table Metadata using AWS CLI Run AWS Athena Queries with a custom location using AWS CLI Drop AWS Athena table using AWS CLI Run CTAS under AWS Athena using AWS CLI Amazon AWS Athena using Python boto3 As part of this section, we will understand how to interact with AWS Athena using Python boto3. Amazon AWS Athena using Python boto3 - Introduction Getting Started with Managing AWS Athena using Python boto3 List Amazon AWS Athena Databases using Python boto3 List Amazon AWS Athena Tables using Python boto3 Run Amazon AWS Athena Queries with boto3 Review AWS Athena Query Results using boto3 Persist Amazon AWS Athena Query Results in Custom Location using boto3 Processing AWS Athena Query Results using Pandas Run CTAS against Amazon AWS Athena using Python boto3 Getting Started with Amazon AWS Redshift As part of this section, we will understand how to get started with AWS Redshift using AWS Web console. We will also focus on basic DDL and DML or CRUD Operations using AWS Redshift Query Editor. Getting Started with Amazon AWS Redshift - Introduction Create AWS Redshift Cluster using Free Trial Connecting to Database using AWS Redshift Query Editor Get a list of tables querying information schema Run Queries against AWS Redshift Tables using Query Editor Create AWS Redshift Table using Primary Key Insert Data into AWS Redshift Tables Update Data in AWS Redshift Tables Delete data from AWS Redshift tables Redshift Saved Queries using Query Editor Deleting AWS Redshift Cluster Restore AWS Redshift Cluster from Snapshot Copy Data from s3 into AWS Redshift Tables As part of this section, we will go through the details about copying data from s3 into AWS Redshift tables using the AWS Redshift Copy command. Copy Data from s3 to AWS Redshift - Introduction Setup Data in s3 for AWS Redshift Copy Copy Database and Table for AWS Redshift Copy Command Create IAM User with full access on s3 for AWS Redshift Copy Run Copy Command to copy data from s3 to AWS Redshift Table Troubleshoot Errors related to AWS Redshift Copy Command Run Copy Command to copy from s3 to AWS Redshift table Validate using queries against AWS Redshift Table Overview of AWS Redshift Copy Command Create IAM Role for AWS Redshift to access s3 Copy Data from s3 to AWS Redshift table using IAM Role Setup JSON Dataset in s3 for AWS Redshift Copy Command Copy JSON Data from s3 to AWS Redshift table using IAM Role Develop Applications using AWS Redshift Cluster As part of this section, we will understand how to develop applications against databases and tables created as part of AWS Redshift Cluster. Develop application using AWS Redshift Cluster - Introduction Allocate Elastic Ip for AWS Redshift Cluster Enable Public Accessibility for AWS Redshift Cluster Update Inbound Rules in Security Group to access AWS Redshift Cluster Create Database and User in AWS Redshift Cluster Connect to the database in AWS Redshift using psql Change Owner on AWS Redshift Tables Download AWS Redshift JDBC Jar file Connect to AWS Redshift Databases using IDEs such as SQL Workbench Setup Python Virtual Environment for AWS Redshift Run Simple Query against AWS Redshift Database Table using Python Truncate AWS Redshift Table using Python Create IAM User to copy from s3 to AWS Redshift Tables Validate Access of IAM User using Boto3 Run AWS Redshift Copy Command using Python AWS Redshift Tables with Distkeys and Sortkeys As part of this section, we will go through AWS Redshift-specific features such as distribution keys and sort keys to create AWS Redshift tables. AWS Redshift Tables with Distkeys and Sortkeys - Introduction Quick Review of AWS Redshift Architecture Create multi-node AWS Redshift Cluster Connect to AWS Redshift Cluster using Query Editor Create AWS Redshift Database Create AWS Redshift Database User Create AWS Redshift Database Schema Default Distribution Style of AWS Redshift Table Grant Select Permissions on Catalog to AWS Redshift Database User Update Search Path to query AWS Redshift system tables Validate AWS Redshift table with DISTSTYLE AUTO Create AWS Redshift Cluster from Snapshot to the original state Overview of Node Slices in AWS Redshift Cluster Overview of Distribution Styles related to AWS Redshift tables Distribution Strategies for retail tables in AWS Redshift Databases Create AWS Redshift tables with distribution style all Troubleshoot and Fix Load or Copy Errors Create AWS Redshift Table with Distribution Style Auto Create AWS Redshift Tables using Distribution Style Key Delete AWS Redshift Cluster with a manual snapshot AWS Redshift Federated Queries and Spectrum As part of this section, we will go through some of the advanced features of Redshift such as AWS Redshift Federated Queries and AWS Redshift Spectrum. AWS Redshift Federated Queries and Spectrum - Introduction Overview of integrating AWS RDS and AWS Redshift for Federated Queries Create IAM Role for AWS Redshift Cluster Setup Postgres Database Server for AWS Redshift Federated Queries Create tables in Postgres Database for AWS Redshift Federated Queries Creating Secret using Secrets Manager for Postgres Database Accessing Secret Details using Python Boto3 Reading Json Data to Dataframe using Pandas Write JSON Data to AWS Redshift Database Tables using Pandas Create AWS IAM Policy for Secret and associate with Redshift Role Create AWS Redshift Cluster using AWS IAM Role with permissions on secret Create AWS Redshift External Schema to Postgres Database Update AWS Redshift Cluster Network Settings for Federated Queries Performing ETL using AWS Redshift Federated Queries Clean up resources added for AWS Redshift Federated Queries Grant Access on AWS Glue Data Catalog to AWS Redshift Cluster for Spectrum Setup AWS Redshift Clusters to run queries using Spectrum Quick Recap of AWS Glue Catalog Database and Tables for AWS Redshift Spectrum Create External Schema using AWS Redshift Spectrum Run Queries using AWS Redshift Spectrum Cleanup the AWS Redshift Cluster | https://www.udemy.com/course/data-engineering-using-aws-analytics-services/#instructor-1 | 20+ years of experience in executing complex projects using a vast array of technologies including Big Data and the Cloud. ITVersity, Inc. - is a US-based organization that provides quality training for IT professionals and we have a track record of training hundreds of thousands of professionals globally. Building an IT career for people with required tools such as high-quality material, labs, live support, etc to upskill and cross-skill is paramount for our organization. At this time our training offerings are focused on the following areas: * Application Development using Python and SQL * Big Data and Business Intelligence * Cloud * Datawarehousing, Databases | Data Engineer | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Artificial Neural Networks (ANN) with Keras in Python and R | Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R | 4.3 | 881 | 158629 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 8h 30m total length | https://www.udemy.com/course/deep-learning-with-keras-and-tensorflow-in-python-and-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create a predictive model using Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 - Python and R basics This part gets you started with Python. This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Part 2 - Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in Python and R In this part you will learn how to create ANN models in Python. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 - Data Preprocessing In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use Python for Deep Learning? Understanding Python is one of the valuable skills needed for a career in Deep Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/deep-learning-with-keras-and-tensorflow-in-python-and-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Neural Networks | >=4 | Below 1K | >=1 Lakh | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Complete Time Series Analysis With Python | Python Time Series: How To Use Data Science, Statistics & Machine Learning For Modelling Time Series Data in Python | 4.3 | 870 | 5180 | Created by Minerva Singh | Jul-21 | English | $9.99 | 4h 14m total length | https://www.udemy.com/course/complete-time-series-data-analysis-bootcamp-with-python/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | THIS IS YOUR COMPLETE GUIDE TO TIME SERIES DATA ANALYSIS IN PYTHON! This course is your complete guide to time series analysis using Python. So, all the main aspects of analyzing temporal data will be covered n depth.. If you take this course, you can do away with taking other courses or buying books on Python based data analysis. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in in analysing time series data in Python, you can give your company a competitive edge and boost your career to the next level. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: Hey, my name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and i have produced many publications for international peer reviewed journals. Over the course of my research I realised almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . So, unlike other instructors, I dig deep into the data science features of R and gives you a one-of-a-kind grounding in data science related topics! You will go all the way from carrying out data reading & cleaning to to finally implementing powerful statistical and machine learning algorithms for analyzing time series data. Among other things: You will be introduced to powerful Python-based packages for time series analysis. You will be introduced to both the commonly used techniques, visualization methods and machine/deep learning techniques that can be implemented for time series data. & you will learn to apply these frameworks to real life data including temporal stocks and financial data. NO PRIOR PYTHON OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED! You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using REAL DATA obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real-life. After taking this course, you’ll easily use the common time series packages in Python... You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data. We will work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW! | https://www.udemy.com/course/complete-time-series-data-analysis-bootcamp-with-python/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Python | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Machine Learning Classification Bootcamp in Python | Build 10 Practical Projects and Advance Your Skills in Machine Learning Using Python and Scikit Learn | 4.6 | 850 | 8426 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Mitchell Bouchard, Ligency I Team, Ligency Team | Nov-22 | English | $11.99 | 11h 43m total length | https://www.udemy.com/course/machine-learning-classification/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning is one of the top skills to acquire in 2022, with an average salary of over $114,000 in the United States, according to PayScale! Over the past two years, the total number of ML jobs has grown around 600 percent and is expected to grow even more by 2025. This course provides students with the knowledge and hands-on experience of state-of-the-art machine learning classification techniques such as Logistic Regression Decision Trees Random Forest Naïve Bayes Support Vector Machines (SVM) This course will provide students with knowledge of key aspects of state-of-the-art classification techniques. We are going to build 10 projects from scratch using a real-world dataset. Here’s a sample of the projects we will be working on: Build an e-mail spam classifier. Perform sentiment analysis and analyze customer reviews for Amazon Alexa products. Predict the survival rates of the titanic based on the passenger features. Predict customer behavior towards targeted marketing ads on Facebook. Predicting bank clients’ eligibility to retire given their features such as age and 401K savings. Predict cancer and Kyphosis diseases. Detect fraud in credit card transactions. Key Course Highlights: This comprehensive machine learning course includes over 75 HD video lectures with over 11 hours of video content. The course contains 10 practical hands-on python coding projects that students can add to their portfolio of projects. No intimidating mathematics, we will cover the theory and intuition in a clear, simple, and easy way. All Jupyter notebooks (codes) and slides are provided. 10+ years of experience in machine learning and deep learning in both academic and industrial settings have been compiled in this course. Students who enroll in this course will master machine learning classification models and can directly apply these skills to solve challenging real-world problems. | https://www.udemy.com/course/machine-learning-classification/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Fundamental Data Analysis and Visualization Tools in Python | Learn how to analyze and visualize data by using Python libraries such as Plotly, Seaborn, Matplotlib, Pandas, and NumPy | 3.7 | 847 | 51328 | Created by James Thomson | Jul-20 | English | $9.99 | 1h 5m total length | https://www.udemy.com/course/data-analysis-and-visualization-tools/ | James Thomson | Programmer & Data Scientist | 3.7 | 926 | 57956 | This course will provide an introduction to the fundamental Python tools for effectively analyzing and visualizing data. You will have a strong foundation in the field of Data Science! You will gain an understanding of how to utilize Python in conjunction with scientific computing and graphing libraries to analyze data, and make presentable data visualizations. This course is designed for both beginners with some basic programming experience or experienced developers looking to explore the world of Data Science! In this course you will: - Learn how to create and analyze data arrays using the NumPy package - Learn how to use the Pandas library to create and analyze data sets - Learn how to use Matplotlib, and Seaborn to create professional, eye-catching data visualizations - Learn how to use Plotly to create interactive charts and plots You will also get lifetime access to all the video lectures, detailed code notebooks for every lecture, as well as the ability to reach out to me anytime for directed inquiries and discussions. | https://www.udemy.com/course/data-analysis-and-visualization-tools/#instructor-1 | Hello! My name is James, and I have experience across various software industries and roles. I'm a programmer who specializes in python, data science, and data analysis. I'm passionate about making content that others learn from and enjoy. Feel free to reach out if you want any help on your journey to becoming a master! | Python | Data Scientist | >=3 | Below 1K | >=50K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
The Complete dbt (Data Build Tool) Bootcamp: Zero to Hero | Learn Analytics Engineering with this dbt™ course covering theory & practice through a real-world Airbnb use case. | Bestseller | 4.6 | 834 | 5437 | Created by Zoltan C. Toth, Miklos (Mike) Petridisz | Nov-22 | English | $9.99 | 5h 14m total length | https://www.udemy.com/course/complete-dbt-data-build-tool-bootcamp-zero-to-hero-learn-dbt/ | Zoltan C. Toth | Data Analytics Architecture Expert | 4.6 | 834 | 5437 | This is the only course you'll need to take to get started with dbt and Analytics Engineering! Greetings to the MOST COMPLETE, CONTINUOUSLY UPDATED independent dbt™ (Data Build Tool) software course in the world - as of 2022! This course is both the TOP RATED and the BESTSELLER dbt course on Udemy! Thank you for joining us for The Complete dbt (Data Build Tool) Bootcamp: Zero to Hero - we are super excited to have you in the course! The structure of the course is designed to have a top-down approach. It starts with the Analytics Engineering Theory - all you need to know is to put dbt (Data Build Tool) in context and to have an understanding of how it fits into the modern data stack. We start with the big picture, then we go deeper and deeper. Once you learned about the pieces, we are going to shift to the technicalities - a practical section -, which will focus on putting together the dbt “puzzle”. The practical section will cover each and every single dbt feature present today through the construction of a complete, real-world project; Airbnb. This presents an opportunity for us to show you which features should be used at what stage in a given project, and you will see how dbt is used in the industry. RECENT UPDATES: Added Great Expectations and test debugging sections - Sep 2022 Radically simplified Windows installation instructions (no WSL needed anymore) - Sep 2022 The course is tested in dbt cloud - Aug 2022 Added Modern Data Stack overview - Jun 2022 THEORETICAL SECTION: Among several other topics, the theoretical section puts special emphasis on transferring knowledge in the following areas; Data-Maturity Model Well-functioning Data Architectures Data Warehouses, Data Lakes, and Data Lakehouses ETL and ELT procedures and Data Transformations Fundamentals of dbt (Data Build Tool) Analytics Engineering Modern Data Stack Slowly Changing Dimensions CTEs Once we understood the theoretical layer and how dbt fits into the picture, we are going to start building out a dbt project from scratch, just as you would do this in the real world. PRACTICAL SECTION: The practical section will go through a real-world Airbnb project where you will master the ins and outs of dbt! We put special focus on getting everyone up and ready before the technical deep dive, hence we will start off by setting up our Development Environment: MAC Development Environment Setup WINDOWS Development Environment Setup IDE dbt Extension Installation Creation and Activation of Virtual Environments Setting up Snowflake Once we are ready - among several other technical topics, the following features will be covered; dbt Models dbt Materializations dbt Tests dbt Documentation dbt Sources, Seeds, Snapshots dbt Hooks and Operations Jinja and Macros dbt Packages Analyses, Exposures dbt Seeds Data Visualization (Preset) Working with Great Expectations (dbt-expectations) Debugging tests in dbt Once the theory and the practical stages are finished, we are going to dive into the best practices and more advanced topics. The course is continuously updated, whenever dbt publishes an update we adjust the course accordingly, so you always be up to date! Who is this course for? Data Engineers Data Analysts Data Scientists BI Developers BI Analyst ... and anyone who interacts with data lake/data warehouse/data lakehouse or uses SQL! Course Level Explained (Zero > Hero) The course doesn't have any expectations about your abilities and starts education from zero. Every exercise is an unavoidable step in your studies. In the same way, don't start an exercise of a superior level without having completed the preceding ones: you will be in difficulty if you do so. Practice is the only way to learn and it cannot be taken lightly. We are going to be next to you along the journey and you have our absolute support! When the Airbnb project is presented to you, you have to do it in its entirety, without omitting any guidelines and by understanding the objective. A project "almost completely" done is often a project "totally incomplete" for us. Give special attention to detail. Your only reliable source of information regarding the instructions is the pedagogical team, don't trust the "I've heard". By the time you complete the course, you will be equipped with both a very solid theoretical understanding and practical expertise with dbt. All the fundamentals, dbt features, best practices, advanced techniques and more will be covered in our course, which will make you become a master in dbt. Are you ready? 😉 How to get help? We just published our initial round of Discussions on Udemy which is the easiest and most efficient way for you to post questions, receive answers, and peruse questions from other students. If you have questions or feedback, please reach out to us! That about wraps it up for us for now! Once again, thank you for being a part of this course. We can't wait to get started with you soon! All the best, Zoltan C. Toth dbt Mark and the dbt logo are trademarks of dbt Labs, Inc. | https://www.udemy.com/course/complete-dbt-data-build-tool-bootcamp-zero-to-hero-learn-dbt/#instructor-1 | I help global companies build web-scale data analytics and AI systems. Backed by 20 years of experience in developing data-intensive applications, I spend most of my time helping companies kick-off and mature their data analytics and AI infrastructure, and give Cloud, Apache Spark, Databricks and MLOps courses regularly. Earlier I built Prezi's big data analytics infrastructure, later led Prezi’s data engineering team, scaling it to serve 60 million users backed by a data volume over a petabyte. I also worked on kicking off the Spark integration component in RapidMiner, a global leader in predictive analytics. Besides working with data analytics architectures, I enjoy teaching at Central European University, one of the best independent universities in Europe, and delivering courses and professional services engagements on behalf of Databricks, the company created by the original authors of Spark. | Misc | Architect | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||
Modern Reinforcement Learning: Deep Q Learning in PyTorch | How to Turn Deep Reinforcement Learning Research Papers Into Agents That Beat Classic Atari Games | 4.5 | 828 | 4233 | Created by Phil Tabor | Oct-20 | English | $12.99 | 5h 41m total length | https://www.udemy.com/course/deep-q-learning-from-paper-to-code/ | Phil Tabor | Machine Learning Engineer | 4.5 | 1271 | 5586 | In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. You will read the original papers that introduced the Deep Q learning, Double Deep Q learning, and Dueling Deep Q learning algorithms. You will then learn how to implement these in pythonic and concise PyTorch code, that can be extended to include any future deep Q learning algorithms. These algorithms will be used to solve a variety of environments from the Open AI gym's Atari library, including Pong, Breakout, and Bankheist. You will learn the key to making these Deep Q Learning algorithms work, which is how to modify the Open AI Gym's Atari library to meet the specifications of the original Deep Q Learning papers. You will learn how to: Repeat actions to reduce computational overhead Rescale the Atari screen images to increase efficiency Stack frames to give the Deep Q agent a sense of motion Evaluate the Deep Q agent's performance with random no-ops to deal with model over training Clip rewards to enable the Deep Q learning agent to generalize across Atari games with different score scales If you do not have prior experience in reinforcement or deep reinforcement learning, that's no problem. Included in the course is a complete and concise course on the fundamentals of reinforcement learning. The introductory course in reinforcement learning will be taught in the context of solving the Frozen Lake environment from the Open AI Gym. We will cover: Markov decision processes Temporal difference learning The original Q learning algorithm How to solve the Bellman equation Value functions and action value functions Model free vs. model based reinforcement learning Solutions to the explore-exploit dilemma, including optimistic initial values and epsilon-greedy action selection Also included is a mini course in deep learning using the PyTorch framework. This is geared for students who are familiar with the basic concepts of deep learning, but not the specifics, or those who are comfortable with deep learning in another framework, such as Tensorflow or Keras. You will learn how to code a deep neural network in Pytorch as well as how convolutional neural networks function. This will be put to use in implementing a naive Deep Q learning agent to solve the Cartpole problem from the Open AI gym. | https://www.udemy.com/course/deep-q-learning-from-paper-to-code/#instructor-1 | In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements for mission critical products. After leaving Intel in 2015, I have worked as a contract and freelance deep learning and artificial intelligence engineer. | PyTorch | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Logistic Regression in Python | Logistic regression in Python tutorial for beginners. You can do Predictive modeling using Python after this course. | 4.7 | 808 | 98700 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 7h 33m total length | https://www.udemy.com/course/machine-learning-basics-classification-models-in-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1543321 | You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in Python, right? You've found the right Classification modeling course! After completing this course you will be able to: Identify the business problem which can be solved using Classification modeling techniques of Machine Learning. Create different Classification modelling model in Python and compare their performance. Confidently practice, discuss and understand Machine Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN Why should you choose this course? This course covers all the steps that one should take while solving a business problem using classification techniques. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a classification model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Classification Machine Learning models: Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation Section 2 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data Pre-processing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation. Section 5 - Classification Models This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a classification model in Python will soar. You'll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Which all classification techniques are taught in this course? In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques: Logistic Regression Linear Discriminant Analysis K - Nearest Neighbors (KNN) How much time does it take to learn Classification techniques of machine learning? Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-basics-classification-models-in-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Python | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Data Science : Master Machine Learning Without Coding | Learn Fundamentals Of Data Science & Machine Learning With Rapidminer (No Coding). Dataset & Solutions Included. | 4.5 | 790 | 5671 | Created by Ram Prasad | Oct-18 | English | $9.99 | 2h 37m total length | https://www.udemy.com/course/hands-on-machine-learning-without-writing-code/ | Ram Prasad | Data Scientist & Certified RapidMiner Analyst | 4.5 | 790 | 6604 | Learn To Master Data Science And Machine Learning Without Coding And Earn a 6-Figure Income Why Data Science and Machine Learning are the Hottest and Most In-Demand Technology Jobs. Data Scientist was recently dubbed “The Sexiest Job of the 21st Century” by Harvard Business Review, and for good reason! If you’re looking for a fast and effective way to earn a 6-figure income without spending thousands of dollars in training, keep reading to learn about this revolutionary Udemy course. Glassdoor reports that Data Scientist was named the “Best Job in America for 2016,” which was based on the huge amount of career opportunities and 6-figure average salary. Business media from Forbes to The New York Times also frequently report about the increasing demand for data scientists. Why is this great news for you? The sudden increase in demand for Data Scientists has created an incredible skills gap in the job market. According to a McKinsey Report, by the end of 2018 the demand for them is expected to be 60% higher than the available talent! Machine Learning is the Key to Your High-Earning Future Leading companies understand that Machine Learning is the future, and are investing millions of dollars into Machine Learning Research. Machine Learning is the subset of Artificial Intelligence (AI) that enables computers to learn and perform tasks they haven’t been explicitly programmed to do. Data Scientists and Machine Learning Engineers who are skilled in Machine Learning are even higher in demand across the entire employment spectrum. Many diverse industries are searching for innovation in the field, and their need for Machine Learning experts and engineers is rapidly increasing. Traditional Machine Learning requires students to know software programming, which enables them to write machine learning algorithms. But in this groundbreaking Udemy course, you’ll learn Machine Learning without any coding whatsoever. As a result, it’s much easier and faster to learn! There’s literally no other course on Udemy that teaches Machine Learning without the need for programming knowledge or coding, using free open source software! A Rare Opportunity to Quickly Learn Data Science and Machine Learning at an Affordable Cost… No Previous Knowledge of Programming Required! Happily, now you can shorten your learning curve and be on your way toward earning a 6-figure income with this groundbreaking Udemy training. Master Machine Learning & Data Science Quickly! One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. A Different & More Effective Approach To Learning Data Science In this groundbreaking course, you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much easier to grasp the fundamental concepts. We’ll Build Several Machine Learning Algorithms Together. I’ll “hand-hold” you as we build from scratch several different types of machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. Learn Both The Theory & Application Of Machine: The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on real world examples. You will learn the theory, but get hands on practice building these machine learning algorithms. You’ll also get access to: · The datasets used in all the exercises. · The solution files of the completed exercises. · Cheat sheets to help you remember the fundamental concepts. Join the class now! | https://www.udemy.com/course/hands-on-machine-learning-without-writing-code/#instructor-1 | Ram develops analytics solutions and predictive models that help long haul trucking companies make critical business decisions in operations, safety and productivity. Ram has 10 years of prior experience in software engineering and has been building data solutions for the last 5 years. He holds a patent (pending) for a text mining application for wireless communication devices in trucks. He is a frequent speaker at Big Data, Data Science conferences and is engaged with the local data science community in Atlanta, Georgia. Ram is a certified RapidMiner Analyst. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
The Data Visualization Course: Excel, Tableau, Python, R | Data visualization in Excel, Tableau, Python, and R. Create stunning charts and learn the most in-demand skills in 2020 | 4.5 | 786 | 7777 | Created by 365 Careers | May-22 | English | $11.99 | 8h 52m total length | https://www.udemy.com/course/the-complete-data-visualization-course/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | Do you want to learn how to create a rich variety of graphs and charts? Do you wish you had superior data interpretation skills? Does your workplace require data visualization proficiency? Yes, yes, and most likely yes. The Complete Data Visualization Course is here for you with TEMPLATES for all the common types of charts and graphs in Excel, Tableau, Python, and R! These are 4 different data visualization courses in 1 course! Whether your preferred environment is Excel, Tableau, Python, or R, this course will enable you to start creating beautiful data visualizations in no time! You will not only learn how to create charts, but also how to label them, style them, and interpret them. Moreover, you will receive immediate access to all templates we work with in the lessons. Simply download the course files, replace the dataset, and amaze your audience! Graphs and charts included in The Complete Data Visualization Course: Bar chart Pie chart Stacked area chart Line chart Histogram Scatter plot Scatter plot with a trendline (regression plot) We live in the age of data. And being able to gather good data, preprocess it, and model it is crucial. However, there is nothing more important than being able to interpret that data. And data visualization allows us to achieve just that. Data visualization is the face of data. Many people look at the data and see nothing. The reason for that is that they are not creating good visualizations. Or even worse – they are creating nice graphs but cannot interpret them accurately. This course will tackle both of these problems. We will make sure you can confidently create any chart that you need to provide a meaningful visualization of the data you are working with. Not only that – you will be able to label and style data visualizations to achieve a ready-for-presentation graph. Furthermore, through this course, you will learn how to interpret different types of charts and when to use them. We will provide examples of great charts as well as terrible charts. We will spare no effort in transforming you into the key person for data visualizations in any team. We are confident that by the time you complete this course, creating and understanding data visualizations will be a piece of cake for you! What makes this course different from the rest of the Data Visualization courses out there? 4 different data visualization courses in 1 course – we cover Excel, Tableau, Python and R Ready-to-use templates for all charts included in the course High-quality production – Full HD and HD video and animations crafted professionally by our experienced team of visual artists Knowledgeable instructor team with experience in teaching on Udemy Complete training – we will cover all common graphs and charts you need to become an invaluable member of your data science team Excellent support - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day Dynamic - we don’t want to waste your time! The instructor sets a very good pace throughout the whole course Why do you need these skills? Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. Literally every company nowadays needs to visualize their data, therefore the data viz position is very well paid Promotions – being the person who creates the data visualizations makes you the bridge between the data and the decision-makers; all stakeholders in the company will value your input, ensuring your spot on the strategy team Secure future – being able to understand data in today’s world is the most important skill to possess and it is only developed by seeing, visualizing and interpreting many datasets Please bear in mind that the course comes with Udemy’s 30-day money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you. Let's start learning together now! | https://www.udemy.com/course/the-complete-data-visualization-course/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Tableau | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||||
Data Visualization in Python Masterclass™: Beginners to Pro | Visualisation in matplotlib, Seaborn, Plotly & Cufflinks, EDA on Boston Housing, Titanic, IPL, FIFA, Covid-19 Data. | 4.7 | 784 | 27308 | Created by Laxmi Kant | Nov-22 | English | $10.99 | 21h 52m total length | https://www.udemy.com/course/complete-data-visualization-in-python/ | Laxmi Kant | Principal Data Scientist at mBreath and KGPTalkie | 4.4 | 1946 | 46559 | Are you ready to start your path to becoming a Data Scientist! KGP Talkie brings you all in one course. Learn all kinds of Data Visualization with practical datasets. This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations! This is a very unique course where you will learn EDA on Kaggle's Boston Housing, Titanic and Latest Covid-19 Datasets, Text Dataset, IPL Cricket Matches of all seasons, and FIFA world cup matches with real and practical examples. Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $110,000 in the United States and all over the World according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 200+ Full HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive courses on Complete Data Visualization in Python. We'll teach you how to program with Python, how to analyze and create amazing data visualizations with Python! You can use this course as your ready-to-go reference for your own project. Here just a few of the topics we will be learning: Programming with Python NumPy with Python Using Pandas Data Frames to solve complex tasks Use Pandas to Files Use matplotlib and Seaborn for data visualizations Use Plotly and Cufflinks for interactive visualizations Exploratory Data Analysis (EDA) of Boston Housing Dataset Exploratory Data Analysis (EDA) of Titanic Dataset Exploratory Data Analysis (EDA) of Latest Covid-19 Dataset and much, much more! By the end of this course you will: Have an understanding of how to program in Python. Know how to create and manipulate arrays using numpy and Python. Know how to use pandas to create and analyze data sets. Know how to use matplotlib and seaborn libraries to create beautiful data visualization. Have an amazing portfolio of python data analysis skills! Have experience of creating a visualization of real-life projects Enroll in the course and become a data scientist today! | https://www.udemy.com/course/complete-data-visualization-in-python/#instructor-1 | I am a Principal Data Scientist at SleepDoc and a Ph.D. in Data Science from the Indian Institute of Technology (IIT). I had also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNC. I have also taught programming at IIT for few years and then later started a YouTube channel, KGP Talkie with 20K+ subscribers. I am very well connected with industry and academia. | Python | Data Scientist | >=4 | Below 1K | >=25K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Machine Learning Real World projects in Python | Build a Portfolio of Machine Learning Python projects & get a job of Data Scientist/ ML Engineer/ Data Scientist | 4.3 | 777 | 74171 | Created by Shan Singh | Nov-22 | English | $9.99 | 13h 7m total length | https://www.udemy.com/course/machine-learning-real-world-projects-in-python/ | Shan Singh | Top Rated & Best-Selling Udemy Instructor , Data Scientist | 4.4 | 5625 | 284385 | Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology. This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition: So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we’ve designed a course to help break it down into real world use-cases that are easier to understand. 1.Task #1 @Predicting the Hotel booking : Predict Whether booking is going to cancel or not 3.Task #2 @Predict Whether Person has a Chronic Disease or not: Develop a Machine learning Model that predicts whether person has kidney disease or not 2.Task #3 @Predict the Prices of Flight: Predict the prices of Flght using Regression & Ensemble Algorithms.. The course covers a number of different machine learning algorithms such as Regression and Classification algorithms. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 3 brand new projects that can help you experience the power of Machine Learning using real-world examples! | https://www.udemy.com/course/machine-learning-real-world-projects-in-python/#instructor-1 | great courses for beginners/working Professionals If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Whether it’s a Data Science , Data Analysis ,Machine Learning , Time Series or Natural Language Processing skills and more here. Query resolution 24*7 One-on-one support from experts that truly want to help you Query resolution (QnA) - Within 2-3 hours in day time Hardly it can be 8-10 hours.. learn by doing Step-by-step tutorials and project-based learning. more about Shan Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you! | Machine Learning | Data Scientist | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Optimization with Python: Solve Operations Research Problems | Solve optimization problems with CPLEX, Gurobi, Pyomo... using linear programming, nonlinear, evolutionary algorithms... | Bestseller | 4.6 | 779 | 5163 | Created by Rafael Silva Pinto | Oct-22 | English | $9.99 | 12h 39m total length | https://www.udemy.com/course/optimization-with-python-linear-nonlinear-and-cplex-gurobi/ | Rafael Silva Pinto | Optimization and Data Science Consultant, PhD | 4.6 | 1448 | 8346 | Operational planning and long term planning for companies are more complex in recent years. Information changes fast, and the decision making is a hard task. Therefore, optimization algorithms (operations research) are used to find optimal solutions for these problems. Professionals in this field are one of the most valued in the market. In this course you will learn what is necessary to solve problems applying Mathematical Optimization and Metaheuristics: Linear Programming (LP) Mixed-Integer Linear Programming (MILP) NonLinear Programming (NLP) Mixed-Integer Linear Programming (MINLP) Genetic Algorithm (GA) Multi-Objective Optimization Problems with NSGA-II (an introduction) Particle Swarm (PSO) Constraint Programming (CP) Second-Order Cone Programming (SCOP) NonConvex Quadratic Programmin (QP) The following solvers and frameworks will be explored: Solvers: CPLEX – Gurobi – GLPK – CBC – IPOPT – Couenne – SCIP Frameworks: Pyomo – Or-Tools – PuLP – Pymoo Same Packages and tools: Geneticalgorithm – Pyswarm – Numpy – Pandas – MatplotLib – Spyder – Jupyter Notebook Moreover, you will learn how to apply some linearization techniques when using binary variables. In addition to the classes and exercises, the following problems will be solved step by step: Optimization on how to install a fence in a garden Route optimization problem Maximize the revenue in a rental car store Optimal Power Flow: Electrical Systems Many other examples, some simple, some complexes, including summations and many constraints. The classes use examples that are created step by step, so we will create the algorithms together. Besides this course is more focused in mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm. Don't worry if you do not know Python or how to code, I will teach you everything you need to start with optimization, from the installation of Python and its basics, to complex optimization problems. Also, I have created a nice introduction on mathematical modeling, so you can start solving your problems. I hope this course can help you in your carrier. Yet, you will receive a certification from Udemy. Operations Research | Operational Research | Mathematical Optimization See you in the classes! | https://www.udemy.com/course/optimization-with-python-linear-nonlinear-and-cplex-gurobi/#instructor-1 | Hello, I love to solve complex problems, my main fields of study are optimization models, artificial intelligence, and data analyses. I am graduated in Electrical Engineering and I hold a PhD degree in optimization models. Also, I'm specialized in data science and big data, and I have a MBA in Business Management. I have worked in a large company in Brazil as data science Manager, and currently, I work as a consultant, helping companies to solve many types of problems. | Python | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | ||||||||||||||||
Complete Data Analysis with Pandas : Hands-on Pandas Python | Learn in demand skill Pandas, Sci-kit Learn, Numpy For Data Science & Machine Learning : Seaborn | MatplotLib | Python | 4.7 | 766 | 12904 | Created by Ankit Mistry, Data Science & Machine Learning Academy | May-21 | English | $12.99 | 16h 51m total length | https://www.udemy.com/course/data-analysis-with-pandas-python/ | Ankit Mistry | Software Developer | I want to Improve your life & Income. | 4.4 | 6134 | 82664 | JOIN OTHER 40,000 SUCCESSFUL STUDENTS WHO HAVE ALREADY ENROLLED & MASTERED PYTHON & PANDAS SKILLS (DATA ANALYSIS LIBRARY) WITH ONE OF MY BEST SELLING, TOP RATED COURSE. Student Testimonial : Great going, ankit is good at explanation of data processing stuff. i bought many of his course related to python and machine learning. - Jay Every concept is clearly explained and the tutor of this course replies to every question asked in Q&A section. - Mukka Akshay It was very good session. The instructor has enough knowledge and able to make me understand clearly. Thank you Ankit! - Bibek Baniya This is an amazing course if you want to understand the extent of the power of Pandas. - Venkat Raj It's one of the best course !!! Most of the topics has been covered and explained up to the expectation - Ankur SIngh it is a good match with what i was looking for, the instructor is quite knowledgeable. - Shivi Dhir This class is not too fast or too slow, the way he teaches is perfect. - Frankie Y It is excellent - Rakhshee Misbah good experience - Weiting ----------------------------------------------------------------------------------------------------------- Update : New section on Data visualization library Matplotlib and Seaborn added. Update : New section on Numpy Library get added. ----------------------------------------------------------------------------------------------------------- If you want to master most in-demand data analysis library pandas, carry on reading. Hi, I am Ankit, one of the Best Selling author on Udemy, taught various courses on Data Science, Python, Pandas, PySpark, Model Deployment. By the end of this course, you will able to apply all majority of Data analysis function on various different datasets with built in function available in pandas. Analysis techniques like exploratory data analysis, data transformation, data wrangling, time series data analysis, analysis through visualization and many more. Carry on reading to know more about course. The era of Microsoft Excel is going to be over, so would you like to learn the next generation one of the most powerful data processing tool and in demand skill required for data analyst, data scientist and data engineer. Then this course is for you, welcome to the course on data analysis with python's most powerful data processing library Pandas. Why this course? Data scientist is one of the hottest skill of 21st century and many organisation are switching their project from Excel to Pandas the advanced Data analysis tool . This course is basically design to get you started with Pandas library at beginner level, covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level. What is this course? This course covers Basics of Pandas library Python crash course for any of you want refresh basic concept of python Python anaconda and Pandas installation Detail understanding about two important data structure available in a Pandas library Series data type Data frame data type How you can group the data for better analysis How to use Pandas for text processing How to visualize the data with Pandas inbuilt visualization tool Multilevel index in Pandas. Time series analysis Numerical Python : NumPy Library Matplotlib and Seaborn for Data visualization Machine Learning Theoretical background Complete end to end Machine Learning Model implementation with Scikit-learn API (from Importing Data to Splitting data, Fitting data and Evaluating Data) & How to Improve Machine Learning Model Importing Data from various different kind of file You will get following after enrolling in this course. 150+ HD quality video lecture 16+ hours of content Discussion forum to resolve your query. quizzes to to test your understanding This course is still in a draft mode. I am still adding more and more content, quiz, projects related to data processing with different functionalities of Pandas. So stay tuned and enroll now. Regards Ankit Mistry | https://www.udemy.com/course/data-analysis-with-pandas-python/#instructor-1 | I am Ankit Mistry, completed my master from IIT Kharagpur in area of machine learning, Artificial intelligence. Now working as Software Developer, Big Data Engineer in one of leading private investment bank with 8+ years of experience in software industry. Over the time I developed interest related to data discipline and learned about data analysis, machine learning model development, Cloud Computing. Created course in area of Cloud Computing, Google Cloud, Python, Data Science, Data analysis, Machine Learning. I am so excited to be on Udemy online learning platform and want to make big impact on your software career. I hope you will like my course offering. | Python | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Complete Data Wrangling & Data Visualisation With Python | Learn to Preprocess, Wrangle and Visualise Data For Practical Data Science Applications in Python | 4.4 | 761 | 14519 | Created by Minerva Singh | Nov-22 | English | $11.99 | 6h 20m total length | https://www.udemy.com/course/complete-data-wrangling-data-visualisation-with-python/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | Hello, My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using statistical modeling and producing publications for international peer reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course! I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science- data wrangling and visualisation. GET ACCESS TO A COURSE THAT IS JAM PACKED WITH TONS OF APPLICABLE INFORMATION! This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. To be more specific, here’s what the course will do for you: (a) It will take you (even if you have no prior statistical modelling/analysis background) from a basic level to performing some of the most common data wrangling tasks in Python. (b) It will equip you to use some of the most important Python data wrangling and visualisation packages such as seaborn. (c) It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results. The course will mostly focus on helping you implement different techniques on real-life data such as Olympic and Nobel Prize winners After each video you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments. TAKE ACTION NOW 🙂 You’ll also have my continuous support when you take this course just to make sure you’re successful with it. If my GUARANTEE is not enough for you, you can ask for a refund within 30 days of your purchase in case you’re not completely satisfied with the course. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. | https://www.udemy.com/course/complete-data-wrangling-data-visualisation-with-python/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Python | Data Scientist | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Text Mining and Natural Language Processing in R | Hands-on text mining and natural language processing (NLP) training for data science applications in R | 4.4 | 749 | 5548 | Created by Minerva Singh | Nov-22 | English | $12.99 | 8h 45m total length | https://www.udemy.com/course/text-mining-and-natural-language-processing-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media? Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends? Over the past decade there has been an explosion in social media sites and now sites like Facebook and Twitter are used for everything from sharing information to distributing news. Social media both captures and sets trends. Mining unstructured text data and social media is the latest frontier of machine learning and data science. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals. Unlike other courses out there, which focus on theory and outdated methods, this course will teach you practical techniques to harness the power of both text data and social media to build powerful predictive models. We will cover web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. Additionally, you will learn to apply both exploratory data analysis and machine learning techniques to gain actionable insights from text and social media data. TAKE YOUR DATA SCIENCE CAREER TO THE NEXT LEVEL BECOME AN EXPERT IN TEXT MINING & NATURAL LANGUAGE PROCESSING : My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like the caret, dplyr to work with real data in R. You will also learn to use the common social media mining and natural language processing packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as identifying important words in a text and predicting movie sentiments based on textual reviews. You will also extract tweets pertaining to trending topics analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful course, you’ll know it all: extracting text data from websites, extracting data from social media sites and carrying out analysis of these using visualization, stats, machine learning, and deep learning! Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: Data Structures and Reading in R, including CSV, Excel, JSON, HTML data. Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Extract and clean data from the FourSquare app Exploratory data analysis of textual data Common Natural Language Processing techniques such as sentiment analysis and topic modelling Implement machine learning techniques such as clustering, regression and classification on textual data Network analysis Plus you will apply your newly gained skills and complete a practical text analysis assignment We will spend some time dealing with some of the theoretical concepts. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! | https://www.udemy.com/course/text-mining-and-natural-language-processing-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | NLP | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Recursion, Backtracking and Dynamic Programming in Python | Learn Competitive Programming, Recursion, Backtracking, Divide and Conquer Methods and Dynamic Programming in Python | 4.6 | 749 | 9295 | Created by Holczer Balazs | Aug-22 | English | $12.99 | 15h 43m total length | https://www.udemy.com/course/algorithmic-problems-in-python/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | This course is about the fundamental concepts of algorithmic problems focusing on recursion, backtracking, dynamic programming and divide and conquer approaches. As far as I am concerned, these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D. Section 1 - RECURSION what are recursion and recursive methods stack memory and heap memory overview what is stack overflow? Fibonacci numbers factorial function tower of Hanoi problem Section 2 - SEARCH ALGORITHMS linear search approach binary search algorithm Section 3 - SELECTION ALGORITHMS what are selection algorithms? Hoare's algorithm how to find the k-th order statistics in O(N) linear running time? quickselect algorithm median of medians algorithm the secretary problem Section 4 - BIT MANIPULATION PROBLEMS binary numbers logical operators and shift operators checking even and odd numbers bit length problem Russian peasant multiplication Section 5 - BACKTRACKING what is backtracking? n-queens problem Hamiltonian cycle problem coloring problem knight's tour problem maze problem Sudoku problem Section 6 - DYNAMIC PROGRAMMING what is dynamic programming? knapsack problem rod cutting problem subset sum problem Kadane's algorithm longest common subsequence (LCS) problem Section 7 - OPTIMAL PACKING what is optimal packing? bin packing problem Section 8 - DIVIDE AND CONQUER APPROACHES what is the divide and conquer approach? dynamic programming and divide and conquer method how to achieve sorting in O(NlogN) with merge sort? the closest pair of points problem Section 9 - Substring Search Algorithms substring search algorithms brute-force substring search Z substring search algorithm Rabin-Karp algorithm and hashing Knuth-Morris-Pratt (KMP) substring search algorithm Section 10 - COMMON INTERVIEW QUESTIONS top interview questions (Google, Facebook and Amazon) anagram problem palindrome problem integer reversion problem dutch national flag problem trapping rain water problem Section 11 - Algorithms Analysis how to measure the running time of algorithms running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations complexity classes polynomial (P) and non-deterministic polynomial (NP) algorithms In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together from scratch in Python. Thanks for joining the course, let's get started! | https://www.udemy.com/course/algorithmic-problems-in-python/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Python | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
From 0 to 1 : Spark for Data Science with Python | Get your data to fly using Spark for analytics, machine learning and data science | 4.2 | 746 | 8008 | Created by Loony Corn | Feb-18 | English | $9.99 | 8h 18m total length | https://www.udemy.com/course/spark-for-data-science-with-python/ | Loony Corn | An ex-Google, Stanford and Flipkart team | 4.2 | 26022 | 153496 | Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. Get your data to fly using Spark for analytics, machine learning and data science Let’s parse that. What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code. Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets. What's Covered: Lot's of cool stuff .. Music Recommendations using Alternating Least Squares and the Audioscrobbler datasetDataframes and Spark SQL to work with Twitter dataUsing the PageRank algorithm with Google web graph datasetUsing Spark Streaming for stream processing Working with graph data using the Marvel Social network dataset .. and of course all the Spark basic and advanced features: Resilient Distributed Datasets, Transformations (map, filter, flatMap), Actions (reduce, aggregate) Pair RDDs , reduceByKey, combineByKey Broadcast and Accumulator variables Spark for MapReduce The Java API for Spark Spark SQL, Spark Streaming, MLlib and GraphFrames (GraphX for Python) | https://www.udemy.com/course/spark-for-data-science-with-python/#instructor-1 | Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you'll like them 🙂 | Spark | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||||
Intro to Big Data, Data Science and Artificial Intelligence | Big Data Technology & Tools for Non-Technical Leaders. Industry expert insights on IoT, AI and Machine Learning for all. | 4.7 | 744 | 1474 | Created by Julia Mariasova | Dec-21 | English | $34.99 | 3h 27m total length | https://www.udemy.com/course/introduction-to-big-data-data-science/ | Julia Mariasova | Management Consultant / Media Producer | 4.7 | 799 | 2000 | If you are like me - finding it difficult to read thick manuals with formulae, but still very much interested in modern technologies and their applications, then this course is for you. You will learn about big data, Internet of Things (IoT), data science, big data technologies, artificial intelligence (AI), machine learning (ML) algorithms, neural networks, and why this could be relevant to you even if you don't have technology or data science background. Please note that this is NOT TECHNICAL TRAINING and it does NOT teach Coding/Development or Statistics. The course includes the interviews with industry experts that cover big data developments in Real Estate, Logistics & Transportation and Healthcare industries. You will learn how machine learning is used to predict engine failures, how artificial intelligence is used in anti-ageing, cancer treatment and clinical diagnosis, you will find out what technology is used in managing smart buildings and smart cities including Hudson Yards in New York. We have got fantastic guest speakers who are the experts in their areas: - WAEL ELRIFAI - Global VP of Solution Engineering - Big Data, IoT & AI at Hitachi Vantara with over 15 years of experience in the field of machine learning and IoT. Wael is also a Co-Authour of the book "The Future of IoT". - ED GODBER - Healthcare Strategist with over 20 years of experience in Healthcare, Pharmaceuticals and start-ups specialising in Artificial Intelligence. - YULIA PAK - Real Estate and Portfolio Strategy Consultant with over 12 years of experience in Commercial Real Estate advisory, currently working with clients who deploy IoT technologies to improve management of their real estate portfolio. Hope you will enjoy the course and let me know in the comments of each section how I can improve the course! | https://www.udemy.com/course/introduction-to-big-data-data-science/#instructor-1 | I have a particular interest in the topics of climate change, decarbonisation, energy transition, digital technologies, data science and machine learning. I believe that even if you are not working in technology or climate change, you still deserve education on these important topics, so that you could be better equipped to deal with new realities and contribute to the society and planet. So I design my own training courses, courses for corporate clients, and offer production services to other instructors/lecturers or organisations. Professionally, I am a management consultant, project, programme and change manager, media producer/director with 20 years of experience in financial services industry (operations and consulting) and 10 years of experience in media/video production industry (educational content and corporate communications). I am passionate about change, strategic development, operational transformation and learning new things. I have experience of working in both large corporate and start-up environments, and running my own business. Masters in Banking and Finance. Qualified Management Accountant (CIMA). Certified in Filmmaking (London Film Academy). Always check my website and social media for the latest discounts. | Artificial Intelligence | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Modern Artificial Intelligence Masterclass: Build 6 Projects | Harness the power of AI to solve practical, real-world problems in Finance, Tech, Art and Healthcare | 4.7 | 734 | 29548 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Jun-21 | English | $11.99 | 15h 46m total length | https://www.udemy.com/course/modern-artificial-intelligence-applications/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | # Course Update June 2021: Added a study on Explainable AI with Zero Coding Artificial Intelligence (AI) revolution is here! “Artificial Intelligence market worldwide is projected to grow by US$284.6 Billion driven by a compounded growth of 43. 9%. Deep Learning, one of the segments analyzed and sized in this study, displays the potential to grow at over 42. 5%.” (Source: globenewswire). AI is the science that empowers computers to mimic human intelligence such as decision making, reasoning, text processing, and visual perception. AI is a broader general field that entails several sub-fields such as machine learning, robotics, and computer vision. For companies to become competitive and skyrocket their growth, they need to leverage AI power to improve processes, reduce cost and increase revenue. AI is broadly implemented in many sectors nowadays and has been transforming every industry from banking to healthcare, transportation and technology. The demand for AI talent has exponentially increased in recent years and it’s no longer limited to Silicon Valley! According to Forbes, AI Skills are among the most in-demand for 2020. The purpose of this course is to provide you with knowledge of key aspects of modern Artificial Intelligence applications in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. The course covers many new topics and applications such as Emotion AI, Explainable AI, Creative AI, and applications of AI in Healthcare, Business, and Finance. One key unique feature of this course is that we will be training and deploying models using Tensorflow 2.0 and AWS SageMaker. In addition, we will cover various elements of the AI/ML workflow covering model building, training, hyper-parameters tuning, and deployment. Furthermore, the course has been carefully designed to cover key aspects of AI such as Machine learning, deep learning, and computer vision. Here’s a summary of the projects that we will be covering: · Project #1 (Emotion AI): Emotion Classification and Key Facial Points Detection Using AI · Project #2 (AI in HealthCare): Brain Tumor Detection and Localization Using AI · Project #3 (AI in Business/Marketing): Mall Customer Segmentation Using Autoencoders and Unsupervised Machine Learning Algorithms · Project #4: (AI in Business/Finance): Credit Card Default Prediction Using AWS SageMaker's XG-Boost Algorithm (AutoPilot) · Project #5 (Creative AI): Artwork Generation by AI · Project #6 (Explainable AI): Uncover the Blackbox nature of AI Who this course is for: The course is targeted towards AI practitioners, aspiring data scientists, Tech enthusiasts, and consultants wanting to gain a fundamental understanding of data science and solve real world problems. Here’s a list of who is this course for: · Seasoned consultants wanting to transform industries by leveraging AI. · AI Practitioners wanting to advance their careers and build their portfolio. · Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business. · Tech enthusiasts who are passionate about AI and want to gain real-world practical experience. Course Prerequisites: Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to anyone with basic programming knowledge. Students who enroll in this course will master data science fundamentals and directly apply these skills to solve real world challenging business problems. | https://www.udemy.com/course/modern-artificial-intelligence-applications/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Artificial Intelligence | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=25K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Practical AI with Python and Reinforcement Learning | Learn how to use Reinforcement Learning techniques to create practical Artificial Intelligence programs! | 4.6 | 731 | 8614 | Created by Jose Portilla | Sep-21 | English | $9.99 | 26h 25m total length | https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Please note! This course is in an "early bird" release, and we're still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete. “The future is already here – it’s just not very evenly distributed.“ Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity? This is the ultimate course online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents! This course focuses on a practical approach that puts you in the driver's seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library! This course covers the following topics: Artificial Neural Networks Convolution Neural Networks Classical Q-Learning Deep Q-Learning SARSA Cross Entropy Methods Double DQN and much more! We've designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning. We'll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks! There is still a lot more to come, I hope you'll join us inside the course! Jose | https://www.udemy.com/course/practical-ai-with-python-and-reinforcement-learning/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Python | Head/Director | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Mathematics & Statistics of Machine Learning & Data Science | Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning | 4.5 | 720 | 8338 | Created by Cinnamon TechX | Aug-22 | English | $9.99 | 11h 7m total length | https://www.udemy.com/course/mathematics-statistics-of-machine-learning-data-science/ | Cinnamon TechX | Providing Breakthrough Learning | 4.5 | 1784 | 13367 | || DATA SCIENCE || Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. What Does a Data Scientist Do? In the past decade, data scientists have become necessary assets and are present in almost all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business. Data scientists need to be curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms. Glassdoor ranked data scientist as the #1 Best Job in America in 2018 for the third year in a row. 4 As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. The growing demand for data science professionals across industries, big and small, is being challenged by a shortage of qualified candidates available to fill the open positions. The need for data scientists shows no sign of slowing down in the coming years. LinkedIn listed data scientist as one of the most promising jobs in 2017 and 2018, along with multiple data-science-related skills as the most in-demand by companies. Where Do You Fit in Data Science? Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data. Data Scientist Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning Data Analyst Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders. Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization Data Engineer Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying. Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop) Data Science Career Outlook and Salary Opportunities Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. With over 4,500 open positions listed on Glassdoor, data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. Predictive causal analytics – If you want a model which can predict the possibilities of a particular event in the future, you need to apply predictive causal analytics. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analytics on the payment history of the customer to predict if the future payments will be on time or not. Prescriptive analytics: If you want a model which has the intelligence of taking its own decisions and the ability to modify it with dynamic parameters, you certainly need prescriptive analytics for it. This relatively new field is all about providing advice. In other terms, it not only predicts but suggests a range of prescribed actions and associated outcomes. The best example for this is Google’s self-driving car which I had discussed earlier too. The data gathered by vehicles can be used to train self-driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take, when to slow down or speed up. Machine learning for making predictions — If you have transactional data of a finance company and need to build a model to determine the future trend, then machine learning algorithms are the best bet. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. For example, a fraud detection model can be trained using a historical record of fraudulent purchases. Machine learning for pattern discovery — If you don’t have the parameters based on which you can make predictions, then you need to find out the hidden patterns within the dataset to be able to make meaningful predictions. This is nothing but the unsupervised model as you don’t have any predefined labels for grouping. The most common algorithm used for pattern discovery is Clustering. Let’s say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength. || DEEP LEARNING || Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before. In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. How does deep learning attain such impressive results? In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images. While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful: Deep learning requires large amounts of labeled data. For example, driverless car development requires millions of images and thousands of hours of video. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less. Examples of Deep Learning at Work Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells. Industrial Automation: Deep learning is helping to improve worker safety around heavy machinery by automatically detecting when people or objects are within an unsafe distance of machines. Electronics: Deep learning is being used in automated hearing and speech translation. For example, home assistance devices that respond to your voice and know your preferences are powered by deep learning applications. What's the Difference Between Machine Learning and Deep Learning? Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically. Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network. A key advantage of deep learning networks is that they often continue to improve as the size of your data increases. || MACHINE LEARNING || What is the definition of machine learning? Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm. Machine learning is the process that powers many of the services we use today—recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on. In all of these instances, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth. WHY IS MACHINE LEARNING SO SUCCESSFUL? While machine learning is not a new technique, interest in the field has exploded in recent years. This resurgence comes on the back of a series of breakthroughs, with deep learning setting new records for accuracy in areas such as speech and language recognition, and computer vision. What's made these successes possible are primarily two factors, one being the vast quantities of images, speech, video and text that is accessible to researchers looking to train machine-learning systems. But even more important is the availability of vast amounts of parallel-processing power, courtesy of modern graphics processing units (GPUs), which can be linked together into clusters to form machine-learning powerhouses. Today anyone with an internet connection can use these clusters to train machine-learning models, via cloud services provided by firms like Amazon, Google and Microsoft. As the use of machine-learning has taken off, so companies are now creating specialized hardware tailored to running and training machine-learning models. An example of one of these custom chips is Google's Tensor Processing Unit (TPU), the latest version of which accelerates the rate at which machine-learning models built using Google's TensorFlow software library can infer information from data, as well as the rate at which they can be trained. These chips are not just used to train models for Google DeepMind and Google Brain, but also the models that underpin Google Translate and the image recognition in Google Photo, as well as services that allow the public to build machine learning models using Google's TensorFlow Research Cloud. The second generation of these chips was unveiled at Google's I/O conference in May last year, with an array of these new TPUs able to train a Google machine-learning model used for translation in half the time it would take an array of the top-end GPUs, and the recently announced third-generation TPUs able to accelerate training and inference even further. As hardware becomes increasingly specialized and machine-learning software frameworks are refined, it's becoming increasingly common for ML tasks to be carried out on consumer-grade phones and computers, rather than in cloud datacenters. In the summer of 2018, Google took a step towards offering the same quality of automated translation on phones that are offline as is available online, by rolling out local neural machine translation for 59 languages to the Google Translate app for iOS and Android. | https://www.udemy.com/course/mathematics-statistics-of-machine-learning-data-science/#instructor-1 | We are an emerging E-Learning company aiming to teach people advanced concepts starting from scratch. We offer real-time projects through real-world data., which help people to develop skills through on-hand one-to-one training. We are currently in the process of creating more online courses and publishing books. We teach: Data Science Personal Development Programming Finance Digital Marketing Stay Tuned for more | Machine Learning | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Docker Masterclass for Machine Learning and Data Science | Learn how to containerize and deploy your ML projects with Docker | 4.2 | 720 | 33738 | Created by Jordan Sauchuk, Ligency I Team, Ligency Team | Sep-22 | English | $11.99 | 4h 23m total length | https://www.udemy.com/course/docker-masterclass-for-machine-learning-and-data-science/ | Jordan Sauchuk | Senior AI Advisor & Cybersecurity Engineer | 4.4 | 23754 | 254848 | Every data scientist is aware that, at some point or another, they'll need to show off their progress and results. And there couldn't be a bigger fear than not having your algorithm run on another computer for reasons you can't define. Enter Docker Masterclass for Machine Learning and Data Science. Led by Docker evangelist and Cybersecurity expert Jordan Sauchuk, this course is designed to get you up and running with Docker, so you will always be prepared to ship your content no matter the situation. You'll learn the ins and outs of Docker, as well as Docker Swarm, Docker Compose, and using Docker with AWS. Also, your Docker path will cover the following steps: A Comprehensive Introduction to Docker Getting the Docker basics down Using Dockerhub Docker Challenges Embedded in each section you'll not only find great video lessons, but also enriching articles and quizzes designed to take you to Docker heaven! On top of that, the content is constantly being updated, so be sure to check every so often for new videos! Once you're done with the course, you'll be certain you can take on any Docker challenges and ship your projects like a pro! So, are you ready to dockerize your ML projects? Enroll now! See you in the classroom. | https://www.udemy.com/course/docker-masterclass-for-machine-learning-and-data-science/#instructor-1 | I'm a Senior AI Advisor, AI & Cybersecurity Engineer, and also the founder of Global Pioneers & the Threat Intelligence Group. My goal is to help provide my experiences and expertise that have been obtained through extensive research, many late nights, and cups of coffee. I have been fortunate enough to work on a range of challenging projects worldwide and I have extensive experience specializing in technologies such as Python, Docker, Kubernetes, AWS, Azure, R, JavaScript, C++, PHP, Tensorflow, Pytorch, Scikit-Learn, Keras, ReactJS, NodeJS, SQL, Plotly, Tesseract, Seaborn, and much more. Recently, I have launched the Threat Intelligence Group as a means of providing cybersecurity awareness, integrating AI into the security domain, and also providing consulting services. For more information please feel free to reach out to me on LinkedIn and Twitter. | Machine Learning | Senior Role | >=4 | Below 1K | >=30K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
PowerBI Zero to Hero | A practical guide to building Dashboards with Power BI | 4.5 | 717 | 33508 | Created by Abdelkarim MOHAMED MAHMOUD | Nov-21 | English | $9.99 | 2h 28m total length | https://www.udemy.com/course/powerbi-hero/ | Abdelkarim MOHAMED MAHMOUD | CTO, PowerBI Specialist, Certified Consultant and Trainer | 4.5 | 717 | 33508 | Hello & Welcome to this Microsoft Power BI course. If you're trying to quickly master the most suitable tool for connecting, transforming, visualising, and analysing your Data, you are in the right place. We will take you from zero to hero, through our practical learning approach, and will quickly get you up running & building analytical solutions for your organisation. My name is Abdelkarim M. MAHMOUD, and I am a Power BI certified Consultant. I have spent the last years empowering different type of organizations by helping them gain performance and competitivity through their Data. I had the chance and the pleasure to train dozens of teams from simple Power BI users to Chief Data Officers. I am here today to put all my expertise at your disposal in a simple and practical way. This in-depth training strikes the perfect balance between theory and practice, several PowerBI use cases are covered to allow you to get the most value from your data. Here's what we're gonna dive into : Introduction - What is Power BI ? More reasons to use Power BI ESPECIALLY if you use excel Resources & Course Structure Quick tour & Interface overview Chapter 1 - Extract, Load, and Transform Data in Power Query Power Query, Connecting to a Database and Data Types Filtering Removing & Ordering Columns Conditional Columns Connecting to a folder as a Data Source Combining Data Dealing with less structured data Fixing Errors Chapter 2 - Data Modeling ? Tables and Relationships BEST PRACTICE : Dimensional Modeling & Star Schemas BEST PRACTICE : Optimising The model for Development Time Chapter 3 - DAX for Data Analysis Expressions DAX, the “POWER” in POWER BI Using Measures to create Calculations and KPIs Comparing this year’s Value to last year’s value Year over Year Varience, and Waterfall Charts Year to Date with DAX Chapter 4 - Data Vizualisation Why Data Visualisation Really Matters Pie Chart and Treemap Hierarchies Filtering and TopN Dual-Axis (Combo) Chart Advances Visual Analytics Pane - Trends, Targets, Forecasts and more Artificial Intelligence: Decomposition Tree, Q&A and Key Influencers Slicers MAPS and Dynamic Coloring ! Fancy Tables Chapter 5 - Power BI Service Sharing and Collaboration in Power BI How to Publish a report to the Power BI service Deep Dive into the Power BI service | https://www.udemy.com/course/powerbi-hero/#instructor-1 | I have worked with multiple types of companies , going from the small business and startups to Fortune 500 multinational corporate companies and NGO's. In 2019, I have co-founded and still am the CTO of DASH - Business Intelligence Lab. where we strive to help organizations increase and improve their performances through data superiority. I have a developed a considerable and specific expertise on Microsoft Power BI, the actual best business intelligence tool worldwide (Gartner 2020). | Power BI | Consultant | >=4 | Below 1K | >=30K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Master Artificial Intelligence 2022 : Build 6 AI Projects | Learn Artificial Intelligence with Python. Create Advanced Artificial Intelligence (AI) Applications with Python | 4.3 | 715 | 45138 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 21h 13m total length | https://www.udemy.com/course/artificial-intelligence-in-python-/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Are you ready to master Artificial Intelligence skills? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. Artificial intelligence (AI) is one of the top tech fields to be in right now! Financial institutions, legal institutions, media companies, and insurance companies are all figuring out ways to use artificial intelligence (ai) to their advantage. From fraud detection to writing news stories with natural language processing(NLP) and reviewing law briefs, AI’s reach is extensive. If you want to build super-powerful applications in artificial intelligence(ai). Then, you are at the right place. This course will provide you with in-depth knowledge on a very hot topic i.e., Artificial Intelligence(AI). The purpose of this course is to provide you with knowledge of key aspects of modern AI without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets. This course will cover the following topics:- 1. Natural Language Processing (NLP). 2. Artificial Neural Network (ANN). 3. Convolutional Neural Network (CNN). 4. Recurrent Neural Network. (RCN) 5. Machine Learning (ML). 6. Deep Learning (DL). This course will take you through the basics to an advanced level in all the mentioned four topics. After taking this course, you will be confident enough to work independently on any projects on these topics. There are lots and lots of exercises for you to practice In this Python Data Science Course and also a 5 Bonus Data Science Project "Sentiment Analysis", "Drug Prescription", "Detecting Pneumonia from X-rays", "Stock Market Prediction", "Fruits Recognition" and "Face emotion Recognition". In this Sentiment Analysis project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights. In this Drug Prescription project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights. In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High Level CNN Architectures. In this Stock Market Prediction project, you will learn to analyze and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models and different Statistical features. In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result. In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear etc. to build a Facial Emotion Detector. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! You will have access to all the resources used in this course. | https://www.udemy.com/course/artificial-intelligence-in-python-/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Artificial Intelligence | Grandmaster | >=4 | Below 1K | >=45K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Artificial Intelligence I: Meta-Heuristics and Games in Java | Graph Algorithms, Genetic Algorithms, Simulated Annealing, Swarm Intelligence, Minimax, Heuristics and Meta-Heuristics | 4.6 | 694 | 7343 | Created by Holczer Balazs | Mar-22 | English | $13.99 | 9h 10m total length | https://www.udemy.com/course/artificial-intelligence-games-in-java/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. - PATHFINDING ALGORITHMS - Section 1 - Breadth-First Search (BFS) what is breadth-first search algorithm why to use graph algorithms in AI Section 2 - Depth-First Search (DFS) what is depth-first search algorithm implementation with iteration and with recursion depth-first search stack memory visualization maze escape application Section 3 - Iterative Deepening Depth-First Search (IDDFS) what is iterative deepening depth-first search algorithm Section 4 - A* Search Algorithm what is A* search algorithm what is the difference between Dijkstra's algorithm and A* search what is a heuristic Manhattan distance and Euclidean distance - OPTIMIZATION - Section 5 - Optimization Approaches basic optimization algorithms brute-force search hill climbing algorithm - META-HEURISTICS - Section 6 - Simulated Annealing what is simulated annealing how to find the extremum of functions how to solve combinatorial optimization problems travelling salesman problem (TSP) Section 7 - Genetic Algorithms what are genetic algorithms artificial evolution and natural selection crossover and mutation solving the knapsack problem Section 8 - Particle Swarm Optimization (PSO) what is swarm intelligence what is the Particle Swarm Optimization algorithm - GAMES AND GAME TREES - Section 9 - Game Trees what are game trees how to construct game trees Section 10 - Minimax Algorithm and Game Engines what is the minimax algorithm what is the problem with game trees? using the alpha-beta pruning approach chess problem Section 11 - Tic Tac Toe with Minimax Tic Tac Toe game and its implementation using minimax algorithm In the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps. Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics. The last topic will be about minimax algorithm and how to use this technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toe game together in the end. Thanks for joining the course, let's get started! | https://www.udemy.com/course/artificial-intelligence-games-in-java/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Artificial Intelligence | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
From 0 to 1: Hive for Processing Big Data | End-to-End Hive : HQL, Partitioning, Bucketing, UDFs, Windowing, Optimization, Map Joins, Indexes | 3.9 | 688 | 6199 | Created by Loony Corn | Jan-18 | English | $12.99 | 15h 15m total length | https://www.udemy.com/course/from-0-to-1-hive/ | Loony Corn | An ex-Google, Stanford and Flipkart team | 4.2 | 26022 | 153496 | Prerequisites: Hive requires knowledge of SQL. The course includes and SQL primer at the end. Please do that first if you don't know SQL. You'll need to know Java if you want to follow the sections on custom functions. Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data. Hive is like a new friend with an old face (SQL). This course is an end-to-end, practical guide to using Hive for Big Data processing. Let's parse that A new friend with an old face: Hive helps you leverage the power of Distributed computing and Hadoop for Analytical processing. It's interface is like an old friend : the very SQL like HiveQL. This course will fill in all the gaps between SQL and what you need to use Hive. End-to-End: The course is an end-to-end guide for using Hive: whether you are analyst who wants to process data or an Engineer who needs to build custom functionality or optimize performance - everything you'll need is right here. New to SQL? No need to look elsewhere. The course has a primer on all the basic SQL constructs, . Practical: Everything is taught using real-life examples, working queries and code . What's Covered: Analytical Processing: Joins, Subqueries, Views, Table Generating Functions, Explode, Lateral View, Windowing and more Tuning Hive for better functionality: Partitioning, Bucketing, Join Optimizations, Map Side Joins, Indexes, Writing custom User Defined functions in Java. UDF, UDAF, GenericUDF, GenericUDTF, Custom functions in Python, Implementation of MapReduce for Select, Group by and Join For SQL Newbies: SQL In Great Depth | https://www.udemy.com/course/from-0-to-1-hive/#instructor-1 | Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you'll like them 🙂 | Big Data/Data Engineer | >=3 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||||
Azure Synapse Analytics For Data Engineers -Hands On Project | Hands on Project for Data Engineers using all the services available in Azure Synapse Analytics [DP-203, DP-500] | Bestseller | 4.6 | 686 | 5813 | Created by Ramesh Retnasamy | Nov-22 | English | $9.99 | 13h 27m total length | https://www.udemy.com/course/azure-synapse-analytics-for-data-engineers/ | Ramesh Retnasamy | Cloud Data Engineer/ Architect | 4.6 | 13410 | 60827 | Welcome! I am looking forward to helping you with learning one of the in-demand data engineering tools in the cloud, Azure Synapse Analytics! This course has been taught with implementing a data engineering solution using Azure Synapse Analytics for a real world project of analysing and reporting on NYC Taxi trips data. This is like no other course in Udemy for Azure Synapse Analytics. Once you have completed the course including all the assignments, I strongly believe that you will be in a position to start a real world data engineering project on your own and also proficient on Azure Synapse Analytics. The primary focus of the course is Azure Synapse Analytics, but it also covers the relevant concepts and connectivity to the other technologies mentioned. The course follows a logical progression of a real world project implementation with technical concepts being explained and the scripts and notebooks being built at the same time. Even though this course is not specifically designed to teach you the skills required for passing the exams Azure Data Engineer Associate Certification [DP-203] or Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI [DP-500], it can greatly help you get most of the necessary skills required for the exams. I value your time as much as I do mine. So, I have designed this course to be fast-paced and to the point. Also, the course has been taught with simple English and no jargons. I start the course from basics and by the end of the course you will be proficient in the technologies used. Currently the course teaches you the following Azure Synapse Analytics Architecture Serverless SQL Pool Spark Pool Dedicated SQL Pool Synapse Pipelines Synapse Link for Cosmos DB / Hybrid Transactional and Analytical Processing (HTAP) capability Power BI Integration with Azure Synapse Analytics Azure Data Lake Storage Gen2 integration with Azure Synapse Analytics Project using NYC Taxi Trips data using the above technologies Please note that the following are not currently covered Data Flows Advanced concepts around Dedicated SQL Pool Spark Programming SQL Fundamentals | https://www.udemy.com/course/azure-synapse-analytics-for-data-engineers/#instructor-1 | Hello! I am a full time senior data engineer/ architect. I have over 20 years of experience delivering some of the large data projects for industries ranging from technology, gaming, finance, retail and government. The projects I delivered were both on cloud platforms such as Azure and AWS as well as On-premises. I am also a Microsoft certified Azure Data Engineer Associate. I have a passion for teaching and I take great pride in the success of my students. I have a different style of teaching than that of a standard I.T. trainer. My courses are based on real world projects. My courses will, not only explain the concepts, but also make them stick by using real world projects and examples. Throughout the course I give guidance on good practices and guide you towards building a production ready application. I strongly believe that once you have completed my courses you will have sufficient experience and knowledge required to start a real time project on that technology. Of course you may require further learning to progress in your career, but I will give you the required foundation and put you in the right direction to gain additional knowledge. I value your time as much as I do mine. So, I keep my courses to the point and my courses have been taught in simple English without any jargons. | Data Engineer | Architect | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
Python for Deep Learning: Build Neural Networks in Python | Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks | 4.3 | 674 | 85631 | Created by Meta Brains | Jan-22 | English | $9.99 | 2h 4m total length | https://www.udemy.com/course/deep-learning-basics-with-python/ | Meta Brains | Let's code & build the metaverse together! | 4.2 | 7800 | 319977 | Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python’s best application is in deep learning and artificial intelligence tasks. While Python makes deep learning easy, it will still be quite frustrating for someone with no knowledge of how machine learning works in the first place. If you know the basics of Python and you have a drive for deep learning, this course is designed for you. This course will help you learn how to create programs that take data input and automate feature extraction, simplifying real-world tasks for humans. There are hundreds of machine learning resources available on the internet. However, you're at risk of learning unnecessary lessons if you don't filter what you learn. While creating this course, we've helped with filtering to isolate the essential basics you'll need in your deep learning journey. It is a fundamentals course that’s great for both beginners and experts alike. If you’re on the lookout for a course that starts from the basics and works up to the advanced topics, this is the best course for you. It only teaches what you need to get started in deep learning with no fluff. While this helps to keep the course pretty concise, it’s about everything you need to get started with the topic. | https://www.udemy.com/course/deep-learning-basics-with-python/#instructor-1 | Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel. We bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together! | Deep Learning | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Complete Linear Algebra for Data Science & Machine Learning | Linear Algebra for Data Science, Big Data, Machine Learning, Engineering & Computer Science. Master Linear Algebra | Bestseller | 4.6 | 671 | 4193 | Created by Kashif A., Abdullah A. | May-21 | English | $13.99 | 17h 54m total length | https://www.udemy.com/course/linear-algebra-for-beginners-matrices-and-vector-spaces/ | Kashif A. | Bestselling Instructor | 4.4 | 5440 | 80351 | DO YOU WANT TO LEARN LINEAR ALGEBRA IN AN EASY WAY? Great! With 22+ hours of content and 200+ video lessons, this course covers everything in Linear Algebra, from start till the end! Every concept is explained in simple language, and Quizzes and Assignments (with solutions!) help you test your concepts as you proceed. Whether you're a student, or a professional or a Math enthusiast, this course walks you through the core concepts of Linear Algebra in an easy and fun way! HERE IS WHAT YOU WILL LEARN: · Fundamentals of Linear Algebra and how to ace your Linear Algebra exam · Basics of matrices, including notation, dimensions, types, addressing the entries etc. · Operations on a single matrix, e.g. scalar multiplication, transpose, determinant, adjoint etc. · Operations on two matrices, including addition, subtraction and multiplication · Performing elementary row operations and finding Echelon Forms (REF & RREF) · Inverses, including invertible and singular matrices, and the Cofactor method · Solving systems of equations using matrices & inverse matrices, including Cramer’s rule to solve AX = B · Performing Gauss-Jordan elimination · Properties of determinants and how to utilize them to gain insights · Matrices as vectors, including vector addition and subtraction, Head-to-Tail rule, components, magnitude and midpoint of a vector · Linear combinations of vectors and span · Vector spaces, including dimensions, Euclidean spaces, closure properties and axioms · Subspace and Null-space of a matrix, matrix-vector products · Spanning set for a vector space and linear dependence · Basis and standard basis, and checking if a set of given vectors forms the basis for a vector space · Eigenvalues and Eigenvectors, including how to find Eigenvalues and the corresponding Eigenvectors · Basic algebra concepts (as a BONUS) · And so much more….. HERE IS WHAT YOU GET IN THE COURSE: Video Lessons: Watch over my shoulder as I explain all the Linear Algebra concepts in a simple and easy to understand language. Everything is taught from scratch, and no prior knowledge is assumed. Solved Examples: Every topic is explained with the help of solved examples, from start to end. This problem-based approach is great, especially for beginners who want to practice their Math concepts while learning. Quizzes: When you think you have understood a concept well, test it by taking the relevant quiz. If you pass, awesome! Otherwise review the suggested lessons and retake the quiz, or ask for help in the Q/A section. Assignments: Multiple assignments offer you a chance for additional practice by solving sets of relevant and insightful problems (with solutions provided) By the end of this course, you'll feel confident and comfortable with all the Linear Algebra topics discussed in this course! WHY SHOULD YOU LEARN LINEAR ALGEBRA? · Linear Algebra is a prerequisite for many lucrative careers, including Data Science, Artificial Intelligence, Machine Learning, Financial Math, Data Engineering etc. · Being proficient in Linear Algebra will open doors for you to many high-in-demand careers WHY LEARN LINEAR ALGEBRA FROM ME? I took this Linear Algebra class at University of Illinois at Urbana Champaign, one of the Top-5 Engineering Schools in the country, and I have tried to follow the same standards while designing this course. I have taught various Math and Engineering courses for more than 10 years at schools across US, Asia and Africa. I strongly believe that I have the ability to breakdown complex concepts into easily understandable chunks of information for you! I provide premium support for all my students - so if you ever get stuck or have a question, just post it to the course dashboard and I'll be there to help you out in a prompt and friendly way! My goal is to make this the best Linear Algebra and Math course online, and I'll do anything possible to help you learn. HERE IS WHAT STUDENTS SAY ABOUT THIS COURSE: “I thoroughly enjoyed this course. I needed to get a better understanding and a good base of Linear Algebra for Data Science and Machine Learning and Kashif absolutely delivered. This is definitely a Zero to Hero course on Linear Algebra in my opinion, and would highly recommend this to anyone who is on the same path as I am. Nothing but appreciation for this author.” – I. Valderrama “Wish I had found this earlier” - Dan “Great explanations. Solid teaching” - J. P. Baugh “Excellent course! The course material is really good, explanation is really clear and every new concept is provided with examples that make the experience even better! The instructor always takes the time to answer questions poster in Q&A. New material is constantly added to course. Thank you!” – K. Geagea YOU'LL ALSO GET: · Lifetime access to “Complete Linear Algebra for Data Science & Machine Learning” · Friendly support in the Q&A section · Udemy Certificate of Completion available for download · 30-day, no-questions-asked, money back guarantee ENROLL TODAY! Feel free to check out the course outline below or watch the free preview lessons. Or go ahead and enroll now. I can’t wait for you to get started with Linear Algebra. Cheers, Kashif | https://www.udemy.com/course/linear-algebra-for-beginners-matrices-and-vector-spaces/#instructor-1 | Kashif holds a Masters in Engineering from a top university in the US. He loves to teach, and has 11 years of university level teaching experience. Apart from traditional academics, he is passionate about Online Education and Digital Entrepreneurship. He is interested in Photo and Video Editing, and Motion Graphics. In his free time, Kashif likes travelling, enjoys cooking and reading books. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Data Science Bootcamp 2022: 5 Data Science Projects | Data Science and Machine Learning Masterclass with Python with 5 Data Science Real World Projects | 4.3 | 666 | 38690 | Created by Data Is Good Academy | Apr-22 | English | $9.99 | 15h 32m total length | https://www.udemy.com/course/data-science-bootcamp-with-python/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Data Science is an interdisciplinary field that uses scientific methods, algorithms to extract clean information from raw data for the formulation of actionable insights. The Data Science field is growing so rapidly, and revolutionizing so many industries. Data Science has incalculable benefits in business, research, and our everyday lives. Your route to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even the health data from your fitness tracker are all important to different data scientists in different ways. Sifting through massive lakes of data, looking for connections and patterns, data science is responsible for bringing us new products, delivering breakthrough insights, and making our lives more convenient. It encompasses a wide range of topics:- Fundamentals of Python. Python Data Structures. Python Functions. Python for Data Science. Data Cleaning. Query Analysis. Data Visualizations using Python. Statistics and Probability. Hypothesis Testing. Data Exploration. Each of these topics are build on the other. You need to acquire all the skills in the right order. You are at the right place!!! Welcome to this online resource to learn Data Science Skills. The Complete Data Science Bootcamp course will really help you to boost your career. This Data Science Course begins with the most basic level and goes up to the most advanced techniques step by step. even if you don't know anything in advance, this course will make complete sense to you. In this Data Science Course you will learn about the following:- 1. The fundamentals of python programming language:- variables, data types, loops and conditionals. 2. Python data structures:- lists, tuples, dictionaries, sets, stacks, queues. 3. Object-oriented programming in python. 4. Regular Expressions. 5. Numpy library. 6. Pandas library. 7. Grouping and filtering operations for data analysis. 8. Basic and Advanced visualizations. 9. Descriptive statistics. 10. Inferential statistics. 11. Hypothesis Testing. 12. Exploring Dabl and Sweetviz library. 13. Linear Regression theory and practical. 14. Logistic Regression theory and practical. 15. Clustering analysis. There are lots and lots of exercises for you to practice In this Python Data Science Course and also a 5 Bonus Data Science Project "Player’s Performance Reviewer", "Start-ups Case Study and Analysis", "Movie Recommender Engine", "Global Cost of Living Analysis" and "Customer Segmentation Engine". In this Player’s Performance Reviewer project, you will analyze the performance metrics of players based on their ground positions, skills, nationality, clubs, age, height, weight, and understanding the major factors driving the performance of these players. In this Start-ups Case Study and Analysis project, you will analyze the Indian Startups, and Understand the Startup Ecosystems in India to answer some Interesting Questions. Try to find out the Major Investors and Startups. In this Movie Recommender Engine project, you will get to learn How to analyze a Movie Database to find some useful insights and Recommend Movies. In this Global Cost of Living Analysis project, you will learn how to perform Geospatial Analysis and understand some major factors determining the quality of life in different cities of the world. And also learn to perform Comparative analysis. In this Customer Segmentation Engine project, you will divide the customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits. You will make use of all the topics read in this Python Data Science Course 2021. You will also have access to all the resources used in this Python Data Science Course 2021. Instructor Support - Quick Instructor Support for any queries. Enroll now and become a Data Science professional!!! | https://www.udemy.com/course/data-science-bootcamp-with-python/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Misc | Grandmaster | >=4 | Below 1K | >=35K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Elasticsearch Masterclass [Incl., Elasticsearch 7 update] | Learn complete Elastic Stack ( Elasticsearch - Logstash - Kibana) & Become a master in Elasticsearch | 4.7 | 665 | 5353 | Created by Vinoth Parthasarathy | Dec-21 | English | $12.99 | 6h 8m total length | https://www.udemy.com/course/elasticsearch-masterclass/ | Vinoth Parthasarathy | Instructor | 4.8 | 7541 | 33929 | Congratulations! You've found the most popular, most complete, and most up-to-date resource online for learning Elasticsearch. Are you interested in the field of Elasticsearch | Big data? Are you interested to play around huge data ? Then this course is for you! The entire course is based around a single goal: Turning you into a professional programmer & capable of handling huge volumes of data as a professional. There are lots of free tutorials and videos on YouTube. Why would you want to take this course? The answer is simple: Quality of teaching. So, from the very beginning to the very end, you'll be confident that you'll be in good hands and watching every minute of the course, unlike the reading many free tutorials and videos, do not waste your precious time. Each section is equipped with a balanced mix of theory and Implementation. It's my goal to make clear about Elasticsearch as much as possible and ensure you're understanding of that. I want everyone to benefit from my courses, that's why we'll dive deeply into Elasticsearch concepts and why I made sure to also share the knowledge that's helpful to programmers Why it’s the only course you need to learn Elasticsearch? This course is everything you need from start to end regardless of your experience. It's an interactive course. Instead of explaining the concepts with boring slides, I will take you to the new version of the training. This course is fun and exciting, but at the same time, we dive deep into Elasticsearch. Specifically, you will learn : * Understanding the core principles of Elasticsearch and Apache Lucene. * The secret behind the elasticsearch fast and how it works under the hood * You will get an in-depth understanding of searching in elasticsearch. * Perform realtime analytics on the huge data and visualize them in Elasticsearch using Kibana * Processing the data by the help of Logstash from numerous sources and to several destinations See what your fellow students have to say "Hats off to Instructor for creating this course for Elasticsearch, in such a wonderful way. He has designed this course covering all possible concepts in much detail, and this is one of the best courses of Elasticsearch. The way the content structured is amazing and the effort he has put to develop this is very much evident" - Munafkhan "I liked this course for two main reasons, firstly the pace at which the instructor delivered the session is excellent, and the other is that the course is transforming boring theoretical content into interactive lectures, which helps in making the concepts more clear" - Omkumar "Excellent materials, and clear explanations. And this is perfect course for getting started with Elasticsearch. Ultimately, it was a good experience for me. Thank you" - Shriman "Outstanding course for Elasticsearch!!! This course contains all the materials for an encapsulated and brief understanding to in-depth knowledge on elasticsearch" - Praveen "Extremely well done. Explanations are thorough with lots of examples and illustrations. I like those concepts and queries are repeated because it really helps to solidify knowledge and make more concrete memories" - Umesh Gupta What if I have questions? As if this course wasn’t complete enough, I offer full support, answering any questions you have 7 days a week. Enroll now and begin your journey towards the most lucrative, adventurous and exciting career path you can imagine! Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you. 100% MONEY-BACK GUARANTEE This course comes with a 30-day full money-back guarantee. Take the course, go through the lectures, do the exercises, and if you're not happy, ask for a refund within 30 days. All your money back, no questions asked. See you on the inside (hurry, Elasticsearch class is waiting!) | https://www.udemy.com/course/elasticsearch-masterclass/#instructor-1 | Hi! I'm Vinoth. I'm creative full stack developer with a serious passionate about technology and teaching the technology to people. And I am working as Senior software engineer and holding around 10 years of experience in software development. Have a great understanding of Data Structures and Algorithms. And also I have a broad set of skills in software, web development, and information technology. Over this 10 years of journey, I have worked on lots of technology and used lots of programming languages like Java, Java Script and Python in the development. The one constant thing in my career has been the need to learn, and keep on learning every day. This is one of the reasons I enjoy teaching and presenting education materials to help other software professionals keep on improving themselves. Sign up to my courses and join me in this amazing adventure today and I'll be there for you every step of the way. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
2022 Machine Learning A to Z : 5 Machine Learning Projects | Learn Complete Machine Learning Bootcamp with Python. Build 5 Complete Machine Learning Real World Projects with Python. | 4.1 | 662 | 39447 | Created by Data Is Good Academy | Apr-22 | English | $9.99 | 26h 15m total length | https://www.udemy.com/course/machine-learning-data-science-python/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Crazy about Data Science and Machine Learning? This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning. Machine Learning is the study of computer algorithms that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is actively being used today, perhaps in many more places than one world expects. It contains a lot of topics and this course will cover all step by step. This Machine Learning course will give you theoretical as well as practical knowledge of Machine Learning. This Machine Learning course is fun as well as exciting. It will cover all common and important algorithms and will give you the experience of working on some real-world projects. This course will cover the following topics:- 1. Theory and practical implementation of linear regression using sklearn. 2. Theory and practical implementation of logistic regression using sklearn. 3. Feature selection using RFECV. 4. Data transformation with linear and logistic regression. 5. Evaluation metrics to analyze the performance of models. 6. Industry relevance of linear and logistic regression. 7. Mathematics behind KNN, SVM, and Naive Bayes algorithms. 8. Implementation of KNN, SVM, and Naive Bayes using sklearn. 9. Attribute selection methods- Gini Index and Entropy. 10. Mathematics behind Decision trees and random forest. 11. Boosting algorithms:- Adaboost, Gradient Boosting, and XgBoost. 12. Different algorithms for clustering. 13. Different methods to deal with imbalanced data. 14. Correlation filtering. 15. Variance filtering. 16. PCA & LDA. 17. Content and Collaborative based filtering. 18. Singular Value decomposition. 19. Different algorithms used for Time Series forecasting. 20. Case studies. We have covered each and every topic in detail and also learned to apply them to real-world problems. There are lots and lots of exercises for you to practice and also a 5 bonus Python Machine Learning Project "Employee Promotion Prediction", "Predicting Medical Health Expenses", "Determining Status for Loan Applicants" and "Optimizing Crop Production". In this Python Machine Learning Employee Promotion Prediction project, you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving of Promotion. Also, learn how to balance Imbalanced Datasets. In this Python Machine Learning Predicting Medical Health Expenses project, you will learn how to Implement a Regression Analysis Predictive Model for Predicting the Future Medical Expenses for People using Linear Regression, Random Forest, Gradient Boosting, etc. In this Python Machine Learning Determining Status for Loan Applicants project, you will learn how to Implement a Classification Analysis Predictive Model for Determining whether a Person should be Granted a Loan or Not. In this Python Machine Learning Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity. You will make use of all the topics read in this course. You will also have access to all the resources used in this course. Enroll now and become a master in machine learning. | https://www.udemy.com/course/machine-learning-data-science-python/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Machine Learning | Grandmaster | >=4 | Below 1K | >=35K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
PySpark Essentials for Data Scientists (Big Data + Python) | Learn how to wrangle Big Data for Machine Learning using Python in PySpark taught by an industry expert! | 4.4 | 660 | 4481 | Created by Layla AI | May-22 | English | $11.99 | 17h 16m total length | https://www.udemy.com/course/pyspark-essentials-for-data-scientists-big-data-python/ | Layla AI | Seasoned Data Scientist Consultant & Passionate Instructor | 4.4 | 660 | 4481 | This course is for data scientists (or aspiring data scientists) who want to get PRACTICAL training in PySpark (Python for Apache Spark) using REAL WORLD datasets and APPLICABLE coding knowledge that you’ll use everyday as a data scientist! By enrolling in this course, you’ll gain access to over 100 lectures, hundreds of example problems and quizzes and over 100,000 lines of code! I’m going to provide the essentials for what you need to know to be an expert in Pyspark by the end of this course, that I’ve designed based on my EXTENSIVE experience consulting as a data scientist for clients like the IRS, the US Department of Labor and United States Veterans Affairs. I’ve structured the lectures and coding exercises for real world application, so you can understand how PySpark is actually used on the job. We are also going to dive into my custom functions that I wrote MYSELF to get you up and running in the MLlib API fast and make getting started building machine learning models a breeze! We will also touch on MLflow which will help us manage and track our model training and evaluation process in a custom user interface that will make you even more competitive on the job market! Each section will have a concept review lecture as well as code along activities structured problem sets for you to work through to help you put what you have learned into action, as well as the solutions to each problem in case you get stuck. Additionally, real world consulting projects have been provided in every section with AUTHENTIC datasets to help you think through how to apply each of the concepts we have covered. Lastly, I’ve written up some condensed review notebooks and handouts of all the course content to make it super easy for you to reference later on. This will be super helpful once you land your first job programming in PySpark! I can’t wait to see you in the lectures! And I really hope you enjoy the course! I’ll see you in the first lecture! | https://www.udemy.com/course/pyspark-essentials-for-data-scientists-big-data-python/#instructor-1 | Layla AI is quickly becoming one of Udemy's leading female instructors in the data science realm. She began her career as a data scientist in 2012 while earning her masters degree in Quantitative Analytics and has been a federal consultant since 2016 for clients like the IRS, Veterans Affairs and Department of Labor. Her skills are most predominantly in predictive modeling, artificial intelligence, natural language processing, topic model, trend analysis, frequent pattern mining, machine-learning, deep-learning, cluster analysis and began teaching in 2020. Her primary programming language is Python but she also has extensive experience with non-object oriented languages like SAS and SQL. Most notably however, she is a passionate teacher who loves to share her knowledge with the world! | Big Data/Data Engineer | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
A Crash Course In PySpark | Learn all the fundamentals of PySpark | 4.6 | 658 | 7938 | Created by Kieran Keene | May-20 | English | $9.99 | 1h 15m total length | https://www.udemy.com/course/a-crash-course-in-pyspark/ | Kieran Keene | Data Engineer at Kodey | 4.6 | 1770 | 36876 | Spark is one of the most in-demand Big Data processing frameworks right now. This course will take you through the core concepts of PySpark. We will work to enable you to do most of the things you’d do in SQL or Python Pandas library, that is: Getting hold of data Handling missing data and cleaning data up Aggregating your data Filtering it Pivoting it And Writing it back All of these things will enable you to leverage Spark on large datasets and start getting value from your data. Let’s get started. | https://www.udemy.com/course/a-crash-course-in-pyspark/#instructor-1 | Hey guys! I am a data engineer by trade and specialize in Python, SQL, Spark, Hive, MongoDB and more. I've come on Udemy to try and make simple, short crash courses into these technologies as I personally find the longer courses too drawn out & I often lose interest. The idea is to keep it short and sharp! For loads of advanced Spark, Python & Big Data topics, please visit my website (the button on this page will take you there) - where I talk about scaling up to enterprise grade solutions. | Spark | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
TensorFlow 2.0 Practical | Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects | 4.6 | 658 | 5997 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Feb-21 | English | $12.99 | 11h 44m total length | https://www.udemy.com/course/tensorflow-2-practical/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice. AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to: (1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions (2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection. (3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification. (4) Develop AI models to perform sentiment analysis and analyze customer reviews. (5) Perform AI models visualization and assess their performance using Tensorboard (6) Deploy AI models in practice using Tensorflow 2.0 Serving The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0. | https://www.udemy.com/course/tensorflow-2-practical/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Tensor Flow | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Applied Time Series Analysis and Forecasting with R Projects | Use R to work on real world time series analysis and forecasting examples. Applied data science with R. | 4.5 | 654 | 4551 | Created by R-Tutorials Training | Jul-18 | English | $9.99 | 3h 21m total length | https://www.udemy.com/course/r-applied-time-series-analysis-forecasting-r-projects-r-tutorials/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | Welcome to the world of R and Time Series Analysis! At the moment R is the leading open source software for time series analysis and forecasting. No other tool, not even python, comes close to the functions and features available in R. Things like exponential smoothing, ARIMA models, time series cross validation, missing data handling, visualizations and forecasts are easily accessible in R and its add on packages. Therefore, R is the right choice for time series analysis and this course gives you an opportunity to train and practice it. So how is the course structured? This is a hands on course with 3 distinct projects to solve! Each project has a main topic and a secondary topic. Both are discussed on real world data. In the first project you work with trending data, and as a secondary topic you will learn how to create standard and ggplot2 time series visualizations. The dataset for that project will be an employment rate dataset. The second project with the German monthly inflation rates over the last 10 years shows how to model seasonal datasets. And you will also compare the models with time series cross validation. In the third project you will connect R to yahoo finance and scrape stock data. The resulting data requires loads of pre-processing and cleaning including missing data imputation. Once we prepared the data, we will check out which weekday is the best for buying and selling the Novartis stock. You should know some R to be able to follow along. There is for example the introduction to time series analysis and forecasting course. That course is more a step by step guide while this one is an applied and project based one. Both courses can be taken on their own, or you take a look at both and learn the subject from 2 different angles. As always you will get the course script as a text file. Of course you get all the standard Udemy benefits like 30 days money back guarantee, lifetime access, instructor support and a certificate for your CV. | https://www.udemy.com/course/r-applied-time-series-analysis-forecasting-r-projects-r-tutorials/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Machine Learning From Basic to Advanced | Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included. | 3.9 | 647 | 110880 | Created by Code Warriors, Anup Mor, Gaurav Sharma, Mayank Bajaj | Aug-21 | English | $9.99 | 2h 59m total length | https://www.udemy.com/course/machine-learning-course/ | Code Warriors | The best place to learn, code and conquer - Once you have it | 4 | 4869 | 305419 | Are you ready to start your path to becoming a Machine Learning Engineer! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Machine Learning as well as Data Scientist! Interested in the field of Machine Learning? Then this course is for you! This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: Part 1 - Data Preprocessing Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression. Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 - Clustering: K-Means, Hierarchical Clustering. And as a bonus, this course includes Python code templates which you can download and use on your own projects. | https://www.udemy.com/course/machine-learning-course/#instructor-1 | Hi, We are Code Warriors an E learning organisation . This is our Udemy Handle where we will provide you some awesome courses with very basic price. The courses will be very much informative and you will enjoy a lot. We focus on your learning in an enjoying manner so you don't get bored. | Machine Learning | >=3 | Below 1K | >=1 Lakh | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Introduction to Artificial Neural Network and Deep Learning | The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition | 4.6 | 642 | 3156 | Created by Seyedali Mirjalili | Apr-20 | English | $11.99 | 7h 2m total length | https://www.udemy.com/course/introduction-to-artificial-neural-network-and-deep-learning/ | Seyedali Mirjalili | PhD in Artificial Intelligence | 4.5 | 3898 | 15231 | Machine learning is an extremely hot area in Artificial Intelligence and Data Science. There is no doubt that Neural Networks are the most well-regarded and widely used machine learning techniques. A lot of Data Scientists use Neural Networks without understanding their internal structure. However, understanding the internal structure and mechanism of such machine learning techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new Neural Networks for different projects. This course is the easiest way to understand how Neural Networks work in detail. It also puts you ahead of a lot of data scientists. You will potentially have a higher chance of joining a small pool of well-paid data scientists. Why learn Neural Networks as a Data Scientist? Machine learning is getting popular in all industries every single month with the main purpose of improving revenue and decreasing costs. Neural Networks are extremely practical machine learning techniques in different projects. You can use them to automate and optimize the process of solving challenging tasks. What does a data scientist need to learn about Neural Networks? The first thing you need to learn is the mathematical models behind them. You cannot believe how easy and intuitive the mathematical models and equations are. This course starts with intuitive examples to take you through the most fundamental mathematical models of all Neural Networks. There is no equation in this course without an in-depth explanation and visual examples. If you hate math, then sit back, relax, and enjoy the videos to learn the math behind Neural Networks with minimum efforts. It is also important to know what types of problems can be solved with Neural Networks. This course shows different types of problems to solve using Neural Networks including classification, regression, and prediction. There will be several examples to practice how to solve such problems as well. What does this course cover? As discussed above, this course starts straight up with an intuitive example to see what a single Neuron is as the most fundamental component of Neural Networks. It also shows you the mathematical and conceptual model of a Neuron. After learning how easy and simple the mathematical models of a single Neuron are, you will see how it performs in action live. The second part of this course covers terminologies in the field of machine learning, a mathematical model of a special type of neuron called Perceptron, and its inspiration. We will go through the main component of a perceptron as well. In the third part, we will work with you on the process of training and learning in Neural networks. This includes learning different error/cost functions, optimizing the cost function, gradient descent algorithm, the impact of the learning rate, and challenges in this area. In the first three parts of this course, you master how a single neuron works (e.g. Perceptron). This prepares you for the fourth part of this course, which is where we will learn how to make a network of these neurons. You will see how powerful even connecting two neurons are. We will learn the impact of multiple neurons and multiple layers on the outputs of a Neural Network. The main model here is a Multi-Layer Perceptron (MLP), which is the most well-regarded Neural Networks in both science and industry. This part of the course also includes Deep Neural Networks (DNN). In the fifth section of this course, we will learn about the Backpropagation (BP) algorithm to train a multi-layer perceptron. The theory, mathematical model, and numerical example of this algorithm will be discussed in detail. All the problems used in Sections 1-5 are classification, which is a very important task with a wide range of real-world applications. For instance, you can classify customers based on their interest in a certain product category. However, there are problems that require prediction. Such problems are solved by regression modes. Neural Networks can play the role of a regression method as well. This is exactly what we will be learning in Section 6 of this course. We start with an intuitive example of doing regression using a single neuron. There is a live demo as well to show how a neuron plays the role of a regression model. Other things that you will learn in this section are: linear regression, logistic (non-linear) regression, regression examples and issues, multiple regressions, and an MLP with three layers to solve any type of repression problems. The last part of this course covers problem-solving using Neural Networks. We will be using Neuroph, which is a Java-based program, to see examples of Neural Networks in the areas and hand-character recognitions and image procession. If you have never used Neuroph before, there is nothing to worry about. There are several videos showing you the steps on how to create and run projects in Neuroph. By the end of this course, you will have a comprehensive understanding of Neural Networks and able to easily use them in your project. You can analyze, tune, and improve the performance of Neural Networks based on your project too. Does this course suit you? This course is an introduction to Neural Networks, so you need absolutely no prior knowledge in Artificial Intelligence, Machine Learning, and AI. However, you need to have a basic understanding of programming especially in Java to easily follow the coding video. If you just want to learn the mathematical model and the problem-solving process using Neural Networks, you can then skip the coding videos. Who is the instructor? I am a leading researcher in the field of Machine Learning with expertise in Neural Networks and Optimization. I have more than 150 publications including 80 journal articles, 3 books, and 20 conference papers. These publications have been cited over 13,000 times around the world. As a leading researcher in this field with over 10 years of experience, I have prepared this course to make everything easy for those interested in Machine Learning and Neural Networks. I have been counseling big companies like Facebook and Google in my career too. I am also a star-rising Udemy instructor with more than 5000 students and 1000 5-star reviews, I have designed and developed this course to facilitate the process of learning Neural Networks for those who are interested in this area. You will have my full support throughout your Neural Networks journey in this course. There is no RISK! I have some preview videos, so make sure to watch them to see if this course is for you. This course comes with a full 30-day money-back guarantee, which means that if you are not happy after your purchase, you can get a 100% refund no question. What are you waiting? Enroll now using the “Add to Cart” button on the right and get started today. | https://www.udemy.com/course/introduction-to-artificial-neural-network-and-deep-learning/#instructor-1 | Professor Seyedali (Ali) Mirjalili is internationally recognized for his advances in Artificial Intelligence (AI) and optimization, including the first set of SI techniques from a synthetic intelligence standpoint - a radical departure from how natural systems are typically understood - and a systematic design framework to reliably benchmark, evaluate, and propose computationally cheap robust optimization algorithms. Prof. Mirjalili has published over 150 journal articles, many in high-impact journals, with one paper having over 4000 citations - the most cited paper in the Elsevier Advances in Engineering Software journal. In addition, he has more than five books, 30 book chapters, and 15 conference papers. Prof. Mirjalili has over 50,000 citations in total with an H-index of 80. From Google Scholar metrics, he is globally one of the most-cited researchers in Artificial Intelligence. As the most cited researcher in Robust Optimization, he is in the list of 1% highly-cited researchers and named as one of the most influential researchers in AI by the world by Web of Science. Ali is a senior member of IEEE and an associate editor of several journals including IEEE Access, Applied Soft Computing, Advances in Engineering Software, and Applied Intelligence. His research interests include Robust Optimization, Engineering Optimization, Multi-objective Optimization, Swarm Intelligence, Evolutionary Algorithms, and Artificial Neural Networks. He is working on the application of multi-objective and robust meta-heuristic optimization techniques as well. In addition to his excellent research outputs, Prof. Ali has been a teacher for over 15 years and a Udemy instructor for more than three years. He has 10,000+ students, and the majority of his courses have been highly ranked by both Udemy and students. He is the only Udemy instructor in the list of top 1% highly-cited researchers. | Deep Learning | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Data Analysis Masterclass (4 courses in 1) | Learn how to build your Data Analysis and Data Visualization skills using Excel, Python, SQL and Tableau with exercises. | 4 | 633 | 23311 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 16h 54m total length | https://www.udemy.com/course/data-analysis-masterclass/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Welcome to the best online course on Data Analysis and Data Visualization. Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. This course is a complete package for everyone wanting to pursue a career in data analysis. This course gives you complete knowledge from basics to advanced level on Excel, Python, and SQL. You will learn to create interactive dashboards using Excel as well as Tableau. In this course, we will cover:- Excel fundamental concepts such as Sorting, Filtering, Statistical, and text functions. Excel functions for data analysis. SQL- DDL, DML, and DQL commands. Python fundamental concepts include Object-oriented programming. Introduction to the NumPy and the pandas' library. Creating charts and dashboards using Tableau. And many more. This course is perfect for beginners who want to make their career in data analysis. You will get to practice a lot of exercises and work on some exciting projects. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! You will also have access to all the resources used in this course. Enroll now and make the best use of this course. | https://www.udemy.com/course/data-analysis-masterclass/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Misc | Grandmaster | >=4 | Below 1K | >=20K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Build Your own Self Driving Car | Deep Learning, OpenCV, C++ | Learn Raspberry Pi, Arduino UNO, Image Processing and Neural Networks (Machine Learning) for any Embedded IOT Project | 4.7 | 629 | 3959 | Created by Rajandeep Singh | Aug-22 | English | $19.99 | 5h 33m total length | https://www.udemy.com/course/selfdrivingcar/ | Rajandeep Singh | Embedded System Engineer | 4.6 | 1591 | 17993 | "Machine Learning will change the lives of all of us. What is Machine Learning? It’s behind what makes self-driving cars a reality" This unique course is a complete walk-through process to Design, Build and Program a Embedded IOT Project (Self driving Car). Everything is discussed with details and clear explanation. Whole Project is divided into 2 parts. (Course - 1) 1. Learn to design complete hardware for self driving car a. Learn to setup Master device ( Raspberry Pi ) for any project b. Learn to setup Slave device ( Arduino UNO ) for any project c. Learn to Establish Communication link between Master and Slave device 2. Learn Image Processing using OpenCV4 3. Learn to driver robot on road lanes (Course - 2) 1. Learn Essentials of Machine Learning 2. Learn to train your own cascade classifier to detect Stop Sign, Traffic Lights and any Object 3. Learn to design LED Dynamic Turn Indicators 4. Create your GitHub Repository Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies | https://www.udemy.com/course/selfdrivingcar/#instructor-1 | Rajandeep Singh is a Embedded System Engineer. He is very proficient in PCB Circuit designing and very passionate for teaching. Moreover, he has a talent and skills to explain complex concepts in very easy way. Expertise: PCB Circuit Designing Analog & Digital Circuits Image Processing Machine Learning C/C++ Programming Over the past 5 years, I have taught many students Embedded Hardware Designing. I have designed many Printed Circuit Boards and programmed numerous embedded project. | Deep Learning | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
R Data Pre-Processing & Data Management - Shape your Data! | Learn how to prepare your data for great analytics in R. | 4.3 | 629 | 4630 | Created by R-Tutorials Training | Nov-18 | English | $11.99 | 6h 25m total length | https://www.udemy.com/course/r-data-management-shape-your-data/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | Let’s get your data in shape! Data Pre-Processing is the very first step in data analytics. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find. With this course I will change this! Data Pre-Processing as taught in this course has the following steps: 1. Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing csv files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle. 2. Selecting the object class: a standard data.frame might be fine for easy standard tasks, but there are more advanced classes out there like the data.table. Especially with those huge datasets nowadays, a data.frame might not do it anymore. Alternatives will be demonstrated in this course. 3. Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format. 4. Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data.table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section. 5. Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time. 6. Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R. 7. Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R. How do you best prepare yourself for this course? You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier. The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course. Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier! | https://www.udemy.com/course/r-data-management-shape-your-data/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Data Analysis Real world use-cases- Hands on Python | Build a Portfolio of 5 Data Analysis Projects with Python, Seaborn,Pandas,Plotly, numpy etc & get a job of Data Analyst | 4.6 | 625 | 64287 | Created by Shan Singh | Nov-22 | English | $9.99 | 6h 54m total length | https://www.udemy.com/course/data-analysis-real-world-use-cases-hands-on-python/ | Shan Singh | Top Rated & Best-Selling Udemy Instructor , Data Scientist | 4.4 | 5625 | 284385 | This is the first course that gives hands-on Data Analysis Projects using Python.. Student Testimonials: This is the best course for people who have just learnt python basics(prerequisite for this course) and want to become Data Analyst/Data Scientist. This will act as bridge between fundamental theoretical python syntax to its application by using most important data analysis packages(Pandas, Matplotlib, Plotly etc). - Mirza Hyder Baig Shan Singh is absolutely amazing! Step-by-step projects with clear explanations. Easy to understand. Real-world Data Analysis projects. Simply the best course on Data Analysis that I could find on Udemy! After the course you can easily start your career as a Data Analyst.- Nicholas Nita A good walkthrough of how python can be used for Data Analysis. Projects 1,2,3, 4 were related to Data Analytics. But, Project 5 was more like a Data Science project since we use nltk, sentimental analysis, text analytics, etc. Overall, a very good course for beginners and those who want to check their python skills for data analysis - Karthik Can you start right now? A frequently asked question of Python Beginners is: "Do I need to become an expert in Python coding before I can start working on Data Analysis Projects?" The clear answer is: "No! You just require some Python Basics like data types, simple operations/operators, lists and numpy arrays that you can learn from my Free Python course 'Basics Of Python' As a Summary, if you primarily want to use Python for Data Science/Data Analytics or as a replacement for Excel, then this course is a perfect match! Why should you take this Course? It explains Real-world Data Analysis Projects on real Data . No toy data! This is the simplest & best way to become a Data Analyst/Data Scientist It shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Data Analysis , Machine Learning and Data Presentation. It gives you plenty of opportunities to practice and code on your own. Learning by doing. In real-world Data Analysis projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion by doing Data Analysis .. Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee. | https://www.udemy.com/course/data-analysis-real-world-use-cases-hands-on-python/#instructor-1 | great courses for beginners/working Professionals If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Whether it’s a Data Science , Data Analysis ,Machine Learning , Time Series or Natural Language Processing skills and more here. Query resolution 24*7 One-on-one support from experts that truly want to help you Query resolution (QnA) - Within 2-3 hours in day time Hardly it can be 8-10 hours.. learn by doing Step-by-step tutorials and project-based learning. more about Shan Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you! | Python | Data Scientist | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Data Science Foundations | Fundamentals of Data Science: Learn the Tools and Techniques | 4.8 | 607 | 3104 | Created by Data Hawk | Nov-21 | English | $9.99 | 3h 4m total length | https://www.udemy.com/course/learn-data-science/ | Data Hawk | Data Science | 4.8 | 610 | 3358 | Welcome to this course on Data Science. An overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data. Data science is an exciting, fast-moving field to become involved in. There's no shortage of demand for talented, analytically-minded individuals. Companies of all sizes are hiring data scientists, and the role provides real value across a wide range of industries and applications. This course covers the necessary tools and concepts used in the data science industry, including machine learning, statistical inference, working with data at scale and much more. We will get into what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists First, we'll start by showing you the entire process for data science projects and the different roles and skills that are needed. Then you'll learn the basics of obtaining data through a variety of sources, including web APIs and page scraping. We'll show you how to use tools like R, Python, the command line, and even spreadsheets to explore and manipulate data. We'll also take a look at powerful techniques for analyzing data. We'll be covering a variety of techniques for planning, performing, and presenting your projects to help you get started in data science and making the most of the data that's all around you. By the end of this course you'll have a solid understanding of what data science is. Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles. | https://www.udemy.com/course/learn-data-science/#instructor-1 | DataHawk provides IT and data science consulting. Our mission is to help businesses unlock the value in their data. We solve complex business problems with high-volume data engineering, analysis, and predictive modeling. We provide data science consulting along and custom development to help you find the best ways to use and implement data science. Our engineers, architects, and data scientists focus on your business challenges to deliver the most suitable solutions. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Complete Course on A/B Testing with Interview Guide | AB Testing, Multivariate Testing, Multi-armed Bandit, Mock Interview Questions, R Coding, Statistics, Hypothesis Testing | 4.4 | 608 | 3359 | Created by Preeti Semwal | Mar-21 | English | $9.99 | 2h 27m total length | https://www.udemy.com/course/product-experimentation-ab-testing-in-r-with-real-examples/ | Preeti Semwal | Data Science & Analytics Leader | 4.4 | 608 | 3359 | Have you always wondered how companies like Google, Facebook, Amazon use experimentation and AB Testing to launch successful products? Do you want to apply online experimentation at your start-up or your current role? Or maybe you are interviewing for a role in big Tech and wondering how to succeed in those interviews? With the rise of smartphones, online controlled testing has really come to the forefront. If you do a google search for Experimentation or A/B testing, you will come across thousands of blogs and articles that discuss this topic. Unfortunately, most of them are either full of inaccuracies and misinterpretation of mathematical concepts Or they are too difficult to understand. This is not surprising. A/B testing is a deep area - there are many nuances involved throughout the process from conceptualization & design all the way to implementation & analysis. This course addresses this. I have designed this course to go deep into important statistical concepts but in a way that is easy to understand using everyday examples. In just two hours, you will learn - What product experimentation is and how to do it right What is AB Testing, Multivariate Testing and Multi-armed Bandit Testing What is the relevance of statistics in AB testing What do statistical concepts such as confidence intervals, Type 1, Type 2 errors, p-value, statistical significance and statistical power mean And how do they fit in the big picture And how to calculate sample size and duration for a successful AB test How to excel in AB testing interviews through real interview questions All these concepts will be reinforced with real world examples from companies such as Amazon, AirBnb, Square and Uber. I will also provide you with templates and cheat sheets that have really helped me in my career. In 2 hours, you can master product experimentation and immediately start applying it in your job or interviews . See you in the course! | https://www.udemy.com/course/product-experimentation-ab-testing-in-r-with-real-examples/#instructor-1 | There is a big shortage of courses that teach Data Science & Analytics in a way that is true to the technical depth but also marry business intuition & implementation really well. My courses address this problem and translate data science through the lens of business implementation, while going deep into the math. That means at the end of the course, not only will you master the mathematical concepts but you will also be ready to apply them in real world setting. I have always been passionate about scientific thinking and deep analysis. My other passion is to teach and nurture. I bring these together as an instructor. I have more than 12 years of experience in Data Science and Analytics working in Silicon Valley for most of those years. Over the years I have worked or consulted with companies such as Deloitte, SoFi, HP, Dell, Groupon, Walmart, Allstate and Mu Sigma. I did MBA from Kellogg School of Management and Masters in Mathematics from Indian Institute of Technology (IIT) Kanpur. I have headed many Marketing and Product Data Science & Analytics teams. In these roles I have helped launch and scale successful products and helped Marketing teams to optimize budgets of hundreds of millions of dollars through data science. Hired, mentored and promoted several managers, data scientists and analysts over the years. My experience includes - Experimentation and A/B testing across Product and Marketing, Multi-touch attribution, market mix modeling, LTV modeling, Customer segmentation, Machine learning models for targeted customer outreach and personalized campaign content/creative, Machine learning models to improve customer acquisition, engagement and retention, Business Intelligence | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Machine Learning in JavaScript with TensorFlow.js | Master machine learning with JavaScript and TensorFlowJS. Add artificial intelligence to websites, Node.js and web apps! | Bestseller | 4.6 | 607 | 4361 | Created by tech.courses team, Justin Emery | Feb-22 | English | $15.99 | 7h 16m total length | https://www.udemy.com/course/machine-learning-in-javascript-with-tensorflow-js/ | tech.courses team | Learn by Doing - Technical Courses, Professionally Delivered | 4.6 | 607 | 4361 | Updated for 2022! Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you! This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2022. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution. This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes. Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps. Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components. This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics: Part 1 - Introduction to TensorFlow.js Part 2 - Installing and running TensorFlow.js Part 3 - TensorFlow.js Core Concepts Part 4 - Data Preparation with TensorFlow.js Part 5 - Defining a model Part 6 - Training and Testing in TensorFlow.js Part 7 - TensorFlow.js Prediction Part 8 - Binary Classification Part 9 - Multi-class Classification Part 10 - Conclusion & Next Steps As a bonus, for every student, we provide you with JavaScript and HTML code templates that you can download and use on your own projects. | https://www.udemy.com/course/machine-learning-in-javascript-with-tensorflow-js/#instructor-1 | Welcome to the tech.courses community, we look forward to providing massive value to you as you learn-by-doing with the best courses and instructors in the world. Our courses cover state-of-the-art technology and software development topics. All instructors are experienced industry professionals who want to share their expertise! See you inside, John, Managing Director | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Apache Spark 3 - Real-time Stream Processing using Python | Learn to create Real-time Stream Processing applications using Apache Spark | 4.7 | 600 | 7154 | Created by Prashant Kumar Pandey, Learning Journal | Jul-21 | English | $9.99 | 4h 35m total length | https://www.udemy.com/course/spark-streaming-using-python/ | Prashant Kumar Pandey | Architect, Author, Consultant, Trainer @ Learning Journal | 4.6 | 14112 | 79992 | About the Course I am creating Apache Spark 3 - Real-time Stream Processing using the Python course to help you understand the Real-time Stream processing using Apache Spark and apply that knowledge to build real-time stream processing solutions. This course is example-driven and follows a working session like approach. We will be taking a live coding approach and explain all the needed concepts along the way. Who should take this Course? I designed this course for software engineers willing to develop a Real-time Stream Processing Pipeline and application using the Apache Spark. I am also creating this course for data architects and data engineers who are responsible for designing and building the organization’s data-centric infrastructure. Another group of people is the managers and architects who do not directly work with Spark implementation. Still, they work with the people who implement Apache Spark at the ground level. Spark Version used in the Course This Course is using the Apache Spark 3.x. I have tested all the source code and examples used in this Course on Apache Spark 3.0.0 open-source distribution. | https://www.udemy.com/course/spark-streaming-using-python/#instructor-1 | Prashant Kumar Pandey is passionate about helping people to learn and grow in their career by bridging the gap between their existing and required skills. In his quest to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. With over 18 years of experience in IT as a developer, architect, consultant, trainer, and mentor, he has worked with international software services organizations on various data-centric and Bigdata projects. Prashant is a firm believer in lifelong continuous learning and skill development. To popularize the importance of lifelong continuous learning, he started publishing free training videos on his YouTube channel and conceptualized the idea of creating a Journal of his learning under the banner of Learning Journal. He is the founder, lead author, and chief editor of the Learning Journal portal that offers various skill development courses, training, and technical articles since the beginning of the year 2018. | Spark | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Machine Learning Regression Masterclass in Python | Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras | 4.5 | 598 | 5491 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Nov-22 | English | $13.99 | 10h 21m total length | https://www.udemy.com/course/machine-learning-regression-masterclass-in-python/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Artificial Intelligence (AI) revolution is here! The technology is progressing at a massive scale and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. Machine Learning is a subfield of Artificial Intelligence that enables machines to improve at a given task with experience. Machine Learning is an extremely hot topic; the demand for experienced machine learning engineers and data scientists has been steadily growing in the past 5 years. According to a report released by Research and Markets, the global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The purpose of this course is to provide students with knowledge of key aspects of machine learning regression techniques in a practical, easy and fun way. Regression is an important machine learning technique that works by predicting a continuous (dependant) variable based on multiple other independent variables. Regression strategies are widely used for stock market predictions, real estate trend analysis, and targeted marketing campaigns. The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including: · Simple Linear Regression · Multiple Linear Regression · Polynomial Regression · Logistic Regression · Decision trees regression · Ridge Regression · Lasso Regression · Artificial Neural Networks for Regression analysis · Regression Key performance indicators The course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems. | https://www.udemy.com/course/machine-learning-regression-masterclass-in-python/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Modern Computer Vision™ PyTorch, Tensorflow2 Keras & OpenCV4 | Using Python Learn OpenCV4, CNNs, Detectron2, YOLOv5, GANs, Tracking, Segmentation, Face Recognition & Siamese Networks | Bestseller | 4.5 | 590 | 6284 | Created by Rajeev D. Ratan | Jun-22 | English | $9.99 | 27h 36m total length | https://www.udemy.com/course/modern-computer-vision/ | Rajeev D. Ratan | Data Scientist, Computer Vision Expert & Electrical Engineer | 4.5 | 8695 | 58239 | Welcome to Modern Computer Vision™ Tensorflow, Keras & PyTorch! AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision! But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless. Job demand for Computer Vision workers are skyrocketing and it’s common that experts in the field are making $200,000+ USD salaries. However, getting started in this field isn’t easy. There’s an overload of information, many of which is outdated, and a plethora of tutorials that neglect to teach the foundations. Beginners thus have no idea where to start. ====================================================== Computer vision applications involving Deep Learning are booming! Having Machines that can 'see' will change our world and revolutionize almost every industry out there. Machines or robots that can see will be able to: Perform surgery and accurately analyze and diagnose you from medical scans. Enable self-driving cars Radically change robots allowing us to build robots that can cook, clean, and assist us with almost any task Understand what's being seen in CCTV surveillance videos thus performing security, traffic management, and a host of other services Create Art with amazing Neural Style Transfers and other innovative types of image generation Simulate many tasks such as Aging faces, modifying live video feeds, and realistically replacing actors in films ====================================================== This course aims to solve all of that! Taught using Google Colab Notebooks (no messy installs, all code works straight away) 27+ Hours of up-to-date and relevant Computer Vision theory with example code Taught using both PyTorch and Tensorflow Keras! In this course, you will learn the essential very foundations of Computer Vision, Classical Computer Vision (using OpenCV) I then move on to Deep Learning where we build our foundational knowledge of CNNs and learn all about the following topics: ====================================================== Detailed OpenCV Guide covering: Image Operations and Manipulations Contours and Segmentation Simple Object Detection and Tracking Facial Landmarks, Recognition and Face Swaps OpenCV implementations of Neural Style Transfer, YOLOv3, SSDs and a black and white image colorizer Working with Video and Video Streams Our Comprehensive Deep Learning Syllabus includes: Classification with CNNs Detailed overview of CNN Analysis, Visualizing performance, Advanced CNNs techniques Transfer Learning and Fine Tuning Generative Adversarial Networks - CycleGAN, ArcaneGAN, SuperResolution, StyleGAN Autoencoders Neural Style Transfer and Google DeepDream Modern CNN Architectures including Vision Transformers (ResNets, DenseNets, MobileNET, VGG19, InceptionV3, EfficientNET and ViTs) Siamese Networks for image similarity Facial Recognition (Age, Gender, Emotion, Ethnicity) PyTorch Lightning Object Detection with YOLOv5 and v4, EfficientDetect, SSDs, Faster R-CNNs, Deep Segmentation - MaskCNN, U-NET, SegNET, and DeepLabV3 Tracking with DeepSORT Deep Fake Generation Video Classification Optical Character Recognition (OCR) Image Captioning 3D Computer Vision using Point Cloud Data Medical Imaging - X-Ray analysis and CT-Scans Depth Estimation Making a Computer Vision API with Flask And so much more This is a comprehensive course, is broken up into two (2) main sections. This first is a detailed OpenCV (Classical Computer Vision tutorial) and the second is a detailed Deep Learning ====================================================== This course is filled with fun and cool projects including these Classical Computer Vision Projects: Sorting contours by size, location, using them for shape matching Finding Waldo Perspective Transforms (CamScanner) Image Similarity K-Means clustering for image colors Motion tracking with MeanShift and CAMShift Optical Flow Facial Landmark Detection with Dlib Face Swaps QR Code and Barcode Reaching Background removal Text Detection OCR with PyTesseract and EasyOCR Colourize Black and White Photos Computational Photography with inpainting and Noise Removal Create a Sketch of yourself using Edge Detection RTSP and IP Streams Capturing Screenshots as video Import Youtube videos directly ====================================================== Deep Learning Computer Vision Projects: PyTorch & Keras CNN Tutorial MNIST PyTorch & Keras Misclassifications and Model Performance Analysis PyTorch & Keras Fashion-MNIST with and without Regularisation CNN Visualisation - Filter and Filter Activation Visualisation CNN Visualisation Filter and Class Maximisation CNN Visualisation GradCAM GradCAMplusplus and FasterScoreCAM Replicating LeNet and AlexNet in Tensorflow2.0 using Keras PyTorch & Keras Pretrained Models - 1 - VGG16, ResNet, Inceptionv3, MobileNetv2, SqueezeNet, WideResNet, DenseNet201, MobileMNASNet, EfficientNet and MNASNet Rank-1 and Rank-5 Accuracy PyTorch and Keras Cats vs Dogs PyTorch - Train with your own data PyTorch Lightning Tutorial - Batch and LR Selection, Tensorboards, Callbacks, mGPU, TPU and more PyTorch Lightning - Transfer Learning PyTorch and Keras Transfer Learning and Fine Tuning PyTorch & Keras Using CNN's as a Feature Extractor PyTorch & Keras - Google Deep Dream PyTorch Keras - Neural Style Transfer + TF-HUB Models PyTorch & Keras Autoencoders using the Fashion-MNIST Dataset PyTorch & Keras - Generative Adversarial Networks - DCGAN - MNIST Keras - Super Resolution SRGAN Project - Generate_Anime_with_StyleGAN CycleGAN - Turn Horses into Zebras ArcaneGAN inference PyTorch & Keras Siamese Networks Facial Recognition with VGGFace in Keras PyTorch Facial Similarity with FaceNet DeepFace - Age, Gender, Expression, Headpose and Recognition Object Detection - Gun, Pistol Detector - Scaled-YOLOv4 Object Detection - Mask Detection - TensorFlow Object Detection - MobileNetV2 SSD Object Detection - Sign Language Detection - TFODAPI - EfficientDetD0-D7 Object Detection - Pot Hole Detection with TinyYOLOv4 Object Detection - Mushroom Type Object Detection - Detectron 2 Object Detection - Website Screenshot Region Detection - YOLOv4-Darknet Object Detection - Drone Maritime Detector - Tensorflow Object Detection Faster R-CNN Object Detection - Chess Pieces Detection - YOLOv3 PyTorch Object Detection - Hardhat Detection for Construction sites - EfficientDet-v2 Object DetectionBlood Cell Object Detection - YOLOv5 Object DetectionPlant Doctor Object Detection - YOLOv5 Image Segmentation - Keras, U-Net and SegNet DeepLabV3 - PyTorch_Vision_Deeplabv3 Mask R-CNN Demo Detectron2 - Mask R-CNN Train a Mask R-CNN - Shapes Yolov5 DeepSort Pytorch tutorial DeepFakes - first-order-model-demo Vision Transformer Tutorial PyTorch Vision Transformer Classifier in Keras Image Classification using BigTransfer (BiT) Depth Estimation with Keras Image Similarity Search using Metric Learning with Keras Image Captioning with Keras Video Classification with a CNN-RNN Architecture with Keras Video Classification with Transformers with Keras Point Cloud Classification - PointNet Point Cloud Segmentation with PointNet 3D Image Classification CT-Scan X-ray Pneumonia Classification using TPUs Low Light Image Enhancement using MIRNet Captcha OCR Cracker Flask Rest API - Server and Flask Web App Detectron2 - BodyPose | https://www.udemy.com/course/modern-computer-vision/#instructor-1 | Hi I'm Rajeev, a Data Scientist, and Computer Vision Engineer. I have a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh where I gained extensive knowledge of machine learning, computer vision, and intelligent robotics. I have published research on using data-driven methods for Probabilistic Stochastic Modeling for Public Transport and even was part of a group that won a robotics competition at the University of Edinburgh. I launched my own computer vision startup that was based on using deep learning in education since then I've been contributing to 2 more startups in computer vision domains and one multinational company in Data Science. Previously, I worked for 8 years at two of the Caribbean’s largest telecommunication operators where he gained experience in managing technical staff and deploying complex telecommunications projects. | PyTorch | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Projects in Machine Learning : Beginner To Professional | A complete guide to master machine learning concepts and create real world ML solutions | 3.7 | 587 | 4880 | Created by Eduonix Learning Solutions, Eduonix-Tech ., Samy Eduonix | Dec-18 | English | $9.99 | 15h 27m total length | https://www.udemy.com/course/machine-learning-for-absolute-beginners/ | Eduonix Learning Solutions | 1+ Million Students Worldwide | 200+ Courses | 3.9 | 90535 | 1 | Update: This course has been updated to include 8 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future! If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter. Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them. So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand. This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer. The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects. The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples! 9 Projects That Are Included in This Course: Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project. Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud. Project 3 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking. Project 4– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network. Project 5 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality. Project 6 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language Processing by solving a text classification task using multiple classification algorithms. Project 7 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset. Project 8 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering. All of this and so much more is included in this course. So, what are you waiting for? Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming. | https://www.udemy.com/course/machine-learning-for-absolute-beginners/#instructor-1 | Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade. We aim to teach technology the way it is used in industry and professional world. We have professional team of trainers for technologies ranging from Mobility, Web to Enterprise and Database and Server Administration. | Machine Learning | >=3 | Below 1K | Below 10K | >=3 | Below 1 Lakh | Million+ | ||||||||||||||||||
RA: Data Science and Supply Chain analytics. A-Z with Python | Learn Python, Supply Chain Data Science ,Linear Programming, Forecasting, Pricing and Inventory Management. | 4.4 | 585 | 4992 | Created by Haytham Omar | Nov-22 | English | $9.99 | 37h 5m total length | https://www.udemy.com/course/ra-data-science-and-supply-chain-analytics-a-z-with-python/ | Haytham Omar | Consultant-Supply chain | 4.4 | 1833 | 55785 | “After our Data Science and supply chain analytics with R course being dubbed the highest rated course in supply chain on Udemy, we are pleased to Introduce Data Science and supply chain analytics. A-Z with Python !! “ " 20000 Professionals are using inventorize across R & Python. Know how to use it only in this course" It's been seven years since I moved from Excel to data science and since then I have never looked back! With eleven years between working in Procurement, lecturing in universities, training over 2000 professionals in supply chain and data science with R and python, and finally opening my own business in consulting for two years now. I am extremely excited to share with you this course and learn with you through this unique rewarding course. My goal is that all of you become experts in data science and supply-chain. I have put all the techniques I have learned and practiced in this one sweet bundle of data science and supply chain. As a consultancy, we develop algorithms for retailers and supply chains to make aggregate and item controllable forecasting, optimize stocks, plan assortment and Maximize profit margin by optimizing prices. 20000 people are already using our free package for supply chain analysis "Inventorize" and we can't wait to share its capabilities with you so you can start dissecting supply chain problems...for free! The motivation behind this project is filling the gap of finding a comprehensive course that tackles supply chains using data science. there are courses for data science, forecasting, revenue management, inventory management, and simulation modeling. but here we tackle all of them as a bundle. Lectures, Concepts, codes, exercises, and spreadsheets. and we don't present the code, we do the code with you, step by step. the abundance of the data from customers, suppliers, products, and transactions have opened the way for making informed business decisions on a bigger and more dynamic scale that can no longer be achieved by spreadsheets. In this course, we learn data science from a supply chain mindset. Don't worry If you don't know how to code, we learn step by step by applying supply chain analysis! *NOTE: Full course includes downloadable resources and Python project files, homework and course quizzes, lifetime access, and a 30-day money-back guarantee. Who this course is for: · If you are an absolute beginner at coding, then take this course. · If you work in a supply-chain and want to make data-driven decisions, this course will equip you with what you need. · If you are an inventory manager and want to optimize inventory for 1000000 products at once, then this course is for you. · If you work in finance and want to forecast your budget by taking trends, seasonality, and other factors into account then this course is just what you need. · If you are a seasoned python user, then take this course to get up to speed quickly with python capabilities. You will become a regular python user in no time. · If you want to take a deep dive (not just talking) in supply chain management, then take this course. · If you want to apply machine learning techniques for supply -chain, we will walk you through the methods of supervised and unsupervised learning. · If you are switching from Excel to a data science language. then this course will fast track your goal. · If you are tired of doing the same analysis again and again on spreadsheets and want to find ways to automate it, this course is for you. · If you are frustrated about the limitations of data loading and available modules in excel, then Moving to python will make our lives a whole lot easier. Course Design the course is designed as experiential learning Modules, the first couple of modules are for supply chain fundamentals followed by Python programming fundamentals, this is to level all of the takers of this course to the same pace. and the third part is supply chain applications using Data science which is using the knowledge of the first two modules to apply. while the course delivery method will be a mix of me explaining the concepts on a whiteboard, Presentations, and Python-coding sessions where you do the coding with me step by step. there will be assessments in most of the sections to strengthen your newly acquired skills. all the practice and assessments are real supply chain use cases. Supply chain Fundamentals Module includes: 1- Introduction to supply chain. 2- Supply chain Flows. 3- Data produced by supply chains. Python Programming Fundamentals Module includes: 1- Basics of Python 2- Data cleaning and Manipulation. 3- Statistical analysis. 4- Data Visualization. 5- Advanced Programming. Supply chain Applications Module include : 1- Product segmentations single and Multi-criteria. 2- Supplier segmentations. 3- Customers segmentations. 4- Forecasting techniques and accuracy testing. 5- Linear Programming and logistics optimizations. 6- Pricing and Markdowns optimization Techniques. 7- Inventory Policy and Safety stock Calculations. 8- Inventory simulations. 9- Machine Learning for supply-chain. 10- Simulations for optimizing Capacity and Resources. *NOTE: Many of the concepts and analysis I explain first in excel as I find excel the best way to first explain a concept and then we scale up, improve and generalize with Python. By the end of this course, you will have an exciting set of skills and a toolbox you can always rely on when tackling supply chain challenges. The course may take from 12-16 weeks to finish, 4-5 hours of lectures, and practice every week. Happy Supply Chain mining! Haytham Rescale Analytics Feedback from Clients and Training: "In Q4 2018, I was fortunate to find an opportunity to learn R in Dubai, after hearing about it from indirect references in UK. I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haitham Omar and the possibilities seemed endless. So, we requested Haitham to conduct a 5-day workshop in our office to train 8 staff members, which opened us up as a team to deeper data analysis. Today, we have gone a step further and retained Haitham, as a consultant, to take our data analysis to the next level and to help us implement inventory guidelines for our business. The above progression of our actions is a clear indication of the capabilities of Haitham as a specialist in R and in data analytics, demand planning, and inventory management." Shailesh Mendonca Commercial lead-in Adventure AHQ- Sharaf Group “ Haytham mentored me in my Role of Head of Supply Chain efficiency. He is extremely knowledgebase about the supply concepts, latest trends, and benchmarks in the supply chain world. Haytham’s analytics-driven approach was very helpful for me to recommend and implement significant changes to our supply chain at Aster group” Saify Naqvi Head of Supply Chain Efficiency “I participated in the training session called "Supply Chain Forecasting & Management" on December 22nd 2018. This training helped me a lot in my daily work since I am working in Purchase Dpt. Haytham have the pedagogy to explain us very difficult calculations and formula in a simple way. I highly recommend this training.” Djamel BOUREMIZ Purchasing Manager at Mineral Circles Bearings | https://www.udemy.com/course/ra-data-science-and-supply-chain-analytics-a-z-with-python/#instructor-1 | "Never follow a book by its cover, In a world that changes every second, we must be resilient and proactive. " Consultant / Developer /Trainer Consultant in Supply Chain Management & Business Intelligence Founder - Rescale Analytics – Dubai • PhD student at the University of Bordeaux. • Supply chain and data science consultant for several national and multinational clients in the UAE and France. • Data Scientist, Master of Science in Global Supply Chain Management from Bordeaux Ecole de Management, Bordeaux, France He is currently conducting workshops and seminars in Supply chain and data science as well as consultancy projects for Sephora, Sharaf group and aster pharmacy. • Haytham conducted more than 70 workshops in UAE in the last three years in supply chain and data science. Clients : Sharaf Group Aster Group Sephora France DNO Qarar for financial analytics Lamprell Noble Prog PWC training Academy Higher College of Technology Partners: Edusphere dubai- Limar Tech Saudi arabia – sl-tech Abu Dhabi Projects :AHQ-SHARAF GROUP: developing an algorithm for the replenishment of stocks to Sharaf group -adventure HQ . the algorithm is being used since November 2019 with notable results. Developing and deploying an algorithm for revenue maximization based on elasticity techniques for adventure zones service provided by AHQ. Projects: Sephora – France Haytham is working with Kedge business school as part of the team CSIT for partnership The collaboration of Omni-channel optimization with Sephora France. As a part of his Ph.D. project. Development: Haytham developed the Inventorize package in R mainly used for supply chain analytics with more than 12000 Downloads so far. | Python | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
No Nonsense Python: learn Python basics and start coding | Because nobody becomes an expert developer by taking a 30 hour course. | 4.7 | 562 | 13897 | Created by Kieran Keene | Feb-20 | English | $9.99 | 58m total length | https://www.udemy.com/course/no-nonsense-python/ | Kieran Keene | Data Engineer at Kodey | 4.6 | 1770 | 36876 | The reason I made this course is simple. When I was learning Python, I would join courses that spent hours covering the most simple of topics and went far too in depth about niche features of Python I would probably not be using for a while. Python is so well documented that if you know the basics, you can find out how to do the advanced things really easily. So this course aims to give you a really good grounding in the basics of Python and will intentionally leave out the bits you're not going to use very often / ever. We will cover: Creating & interacting with variables Using arithmetic functions Using comparison operators String functions Creating & interacting with lists, tuples and dictionaries If statements For & while loops Functions | https://www.udemy.com/course/no-nonsense-python/#instructor-1 | Hey guys! I am a data engineer by trade and specialize in Python, SQL, Spark, Hive, MongoDB and more. I've come on Udemy to try and make simple, short crash courses into these technologies as I personally find the longer courses too drawn out & I often lose interest. The idea is to keep it short and sharp! For loads of advanced Spark, Python & Big Data topics, please visit my website (the button on this page will take you there) - where I talk about scaling up to enterprise grade solutions. | Python | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Computer Vision Masterclass | Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python! | 4.5 | 552 | 28336 | Created by Jones Granatyr, Ligency I Team, Ligency Team, Gabriel Alves, IA Expert Academy | Aug-22 | English | $9.99 | 25h 28m total length | https://www.udemy.com/course/computer-vision-masterclass/ | Jones Granatyr | Professor | 4.7 | 34416 | 158021 | Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered. In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement: Detect faces in images and videos using OpenCV and Dlib libraries Learn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib libraries Track objects in videos using KCF and CSRT algorithms Learn the whole theory behind artificial neural networks and implement them to classify images Implement convolutional neural networks to classify images Use transfer learning and fine tuning to improve the results of convolutional neural networks Detect emotions in images and videos using neural networks Compress images using autoencoders and TensorFlow Detect objects using YOLO, one of the most powerful techniques for this task Recognize gestures and actions in videos using OpenCV Create hallucinogenic images using the Deep Dream technique Combine style of images using style transfer Create images that don't exist in the real world with GANs (Generative Adversarial Networks) Extract useful information from images using image segmentation You are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab | https://www.udemy.com/course/computer-vision-masterclass/#instructor-1 | Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios. | Computer Vision | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=25K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Crash course: Data analytics in Python using Pandas | Let's get to grips with the Python Pandas library for data analytics / analysis | 4.3 | 550 | 21208 | Created by Kieran Keene | Jan-20 | English | $9.99 | 59m total length | https://www.udemy.com/course/python-analytics/ | Kieran Keene | Data Engineer at Kodey | 4.6 | 1770 | 36876 | The demand for data engineers is greater than ever. So, there is no better time to upskill; learn Python and specifically data engineering. I'll take you through the core concepts of dataframes, which are a key data structure within Pandas. We'll learn to ingest, clean and analyse the data and by the end of the course, you'll be in a position to use Python & Pandas on your own data to extract valuable insight. The idea isn’t to become an expert through this course. The idea is to become confident in the core concepts of Python and Pandas so you can solve real-world problems today and so you can continue your learning by doing. Because, nobody becomes an expert through taking a course (no matter how long they are), you only truly become an expert by getting out there & solving problems. For this course, you'll need some basic Python knowledge, which you can gain from my FREE No Nonsense Python course here on Udemy. You will need to have Python installed and the Pandas library installed - which you can do using 'pip install pandas'. | https://www.udemy.com/course/python-analytics/#instructor-1 | Hey guys! I am a data engineer by trade and specialize in Python, SQL, Spark, Hive, MongoDB and more. I've come on Udemy to try and make simple, short crash courses into these technologies as I personally find the longer courses too drawn out & I often lose interest. The idea is to keep it short and sharp! For loads of advanced Spark, Python & Big Data topics, please visit my website (the button on this page will take you there) - where I talk about scaling up to enterprise grade solutions. | Data Analyst | Engineer/Developer | >=4 | Below 1K | >=20K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Data Science and Machine Learning using Python - A Bootcamp | Numpy Pandas Matplotlib Seaborn Ploty Machine Learning Scikit-Learn Data Science Recommender system NLP Theory Hands-on | 4.1 | 545 | 2494 | Created by Dr. Junaid Qazi, PhD | Feb-20 | English | $9.99 | 24h 57m total length | https://www.udemy.com/course/data-science-and-machine-learning-using-python-bootcamp-qazi/ | Dr. Junaid Qazi, PhD | Data Scientist | 4.1 | 545 | 7185 | Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing? This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course: Basis Python programming for Data Science Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter NumPy Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions Pandas Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization Matplotlib Basic Plotting & Object Oriented Approach Seaborn Distribution & Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics Plotly and Cufflinks Interactive & Geographical plotting SciKit-Learn (one of the world's best machine learning Python library) including: Liner Regression Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models Logistic Regression Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision K Nearest Neighbour (KNN) Curse of Dimensionality, Model Performance Decision Trees Tree Depth, Splitting at Nodes, Entropy, Information Gain Random Forests Bootstrap, Bagging (Bootstrap Aggregation) K Mean Clustering Elbow Method Principle Component Analysis (PCA) Support Vector Machine Recommender Systems Natural Language Processing (NLP) Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature........and MUCH MORE..........! Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models. So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course.....! Brief overview of Data around us: According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in-depth skills of data analysis and presentation. Have Fun and Good Luck! | https://www.udemy.com/course/data-science-and-machine-learning-using-python-bootcamp-qazi/#instructor-1 | Dr. Qazi has a BS with major in Maths, Statistics & Physics, MS in Computer Science and PhD degree. As a mentor and a researcher scientist, with over 18 years of professional experience, Dr. Qazi has developed a skill set in data cleaning/mining, data analysis & data modelling, project management, teaching & training and career advising while working with academic and industrial giants. Dr. Qazi has also served in academia for several years at the rank of lecturer and assistant professor. During his career, he won several funding awards for his research ideas and published high quality articles in well reputed international journals in collaboration with leading scientists from University of British Columbia, Canada; University of Calgary, Canada; Uppsala University, Sweden; Institut Laue-Langevin (ILL), France; European Synchrotron Radiation Facility (ESRF), France; Diamond Light Source, UK; ISIS Neutron and Muon Source, UK; Unilever, UK; NIST Centre for Neutron Research, USA. He has developed algorithms and written computer codes for his published work. As an invited speaker, Dr. Qazi has delivered several lectures around the globe to the scientific and business community. Diverse academic background along with educational journey across Asia, Europe and North America has honed Dr. Qazi with adaptability, determination and internationalization. As a passionate professional, He has always been ready to learn new techniques to fulfil the needs of his students. He has participated in several professional trainings on teaching and project management. Now, Dr Qazi believes that he can use his experience to help his students acquire the skills to analyze data and present a clear story line with beautiful visualizations in their reports to facilitate industry leaders in decision making. Dr. Qazi look forward to see you in his course on Data Science and Machine Learning. Good Luck! P.S. Dr. Qazi can be reached via LinkedIn for more information on in-person and group training. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Machine Learning Deep Learning model deployment | Serving TensorFlow Keras PyTorch Python model Flask Serverless REST API MLOps MLflow Cloud GCP NLP tensorflow.js deploy | 4.2 | 545 | 9679 | Created by FutureX Skills | Nov-21 | English | $9.99 | 4h 24m total length | https://www.udemy.com/course/machine-learning-deep-learning-model-deployment/ | FutureX Skills | Data AI evangelists | 4.3 | 2683 | 45656 | In this course you will learn how to deploy Machine Learning Models using various techniques. Course Structure: Creating a Model Saving a Model Exporting the Model to another environment Creating a REST API and using it locally Creating a Machine Learning REST API on a Cloud virtual server Creating a Serverless Machine Learning REST API using Cloud Functions Deploying TensorFlow and Keras models using TensorFlow Serving Deploying PyTorch Models Converting a PyTorch model to TensorFlow format using ONNX Creating REST API for Pytorch and TensorFlow Models Deploying tf-idf and text classifier models for Twitter sentiment analysis Deploying models using TensorFlow.js and JavaScript Tracking Model training experiments and deployment with MLfLow Python basics and Machine Learning model building with Scikit-learn will be covered in this course. You will also learn how to build and deploy a Neural Network using TensorFlow Keras and PyTorch. Google Cloud (GCP) free trial account is required to try out some of the labs designed for cloud environment. | https://www.udemy.com/course/machine-learning-deep-learning-model-deployment/#instructor-1 | We are a group of Solution Architects and Developers with expertise in Java, Python, Scala , Big Data , Machine Learning and Cloud. We have years of experience in building Data and Analytics solutions for global clients. Our primary goal is to simplify learning for our students. We take a very practical use case based approach in all our courses. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Deep Learning with PyTorch for Medical Image Analysis | Learn how to use Pytorch-Lightning to solve real world medical imaging tasks! | 4.4 | 546 | 5511 | Created by Jose Portilla, Marcel Früh, Sergios Gatidis, Tobias Hepp | Nov-22 | English | $9.99 | 12h 5m total length | https://www.udemy.com/course/deep-learning-with-pytorch-for-medical-image-analysis/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs? Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images? Then this is the right course for you! Welcome to one of the most comprehensive courses on Deep Learning in medical imaging! This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges. You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more. The following topics are covered: NumPy Machine Learning Theory Test/Train/Validation Data Splits Model Evaluation - Regression and Classification Tasks Tensors with PyTorch Convolutional Neural Networks Medical Imaging Interpretability of a network's decision - Why does the network do what it does? A state of the art high level pytorch library: pytorch-lightning Tumor Segmentation Three-dimensional data and many more Why choose this specific Deep Learning with PyTorch for Medical Image Analysis course ? This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server. You will learn skills and techniques that the vast majority of AI engineers do not have! -------------- Jose, Marcel, Sergios & Tobias | https://www.udemy.com/course/deep-learning-with-pytorch-for-medical-image-analysis/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | PyTorch | Head/Director | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Apache Spark Streaming 3 with Scala | Rock the JVM | Stream big data with Apache Spark Streaming 3 and integrate with Kafka, JDBC, Cassandra and more, hands-on, in Scala | 4.6 | 546 | 5074 | Created by Daniel Ciocîrlan | Feb-21 | English | $10.99 | 7h 12m total length | https://www.udemy.com/course/spark-streaming/ | Daniel Ciocîrlan | Software Engineer & Best-Selling Instructor | 4.7 | 30097 | 96958 | In this course, we will learn how to stream big data with Apache Spark 3. You'll write 1500+ lines of Spark code yourself, with guidance, and you will become a rockstar. This course is for Spark & Scala programmers who now need to work with streaming data, or who need to process data in real time. Why Spark in Scala: it's blazing fast for big data its demand has exploded it's a highly marketable skill it's well maintained, with dozens of high-quality extensions it's a foundation for a data scientist I like to get to the point and get things done. This course deconstructs all concepts into the critical pieces you need selects the most important ideas and separates them into what's simple but critical and what's powerful sequences ideas in a way that "clicks" and makes sense throughout the process of learning applies everything in live code The end benefits are still much greater: a completely new mental model around data streaming significantly more marketable resume more enjoyable work - Spark is fun! This course is for established programmers with experience with both Scala and Spark at least at the level of the Rock the JVM essential courses for Scala and Spark. I already assume a solid understanding of general programming fundamentals. This course is NOT for you if you've never written Scala or Spark code before you don't have some essential parallel programming background (e.g. what's a process/a thread) The course is comprehensive, but you'll always see me get straight to the point. So make sure you have a good level of focus and commitment to become a badass programmer. I believe both theory and practice are important. That's why you'll get lectures with code examples, real life code demos and assignments, plus additional resources, instructions, exercises and solutions. At the end of the course, you'll have written thousands of lines of Spark. I've seen that my students are most successful - and my best students work at Google-class companies - when they're guided, but not being told what to do. I have exercises waiting for you, where I offer my (opinionated) guidance but otherwise freedom to experiment and improve upon your code. Definitely not least, my students are most successful when they have fun along the way! So join me in this course and let's rock the JVM! | https://www.udemy.com/course/spark-streaming/#instructor-1 | I'm a software engineer with a passion for teaching. Big fan of Scala and the JVM. I have a Master's Degree in Computer Science and I wrote my Bachelor and Master theses on Quantum Computation. Before starting to learn programming, I won medals at international Physics competitions. For 7+ years, I've taught a variety of Computer Science topics to 30000+ of students at various levels. I've held Hour of Code for 7 year-olds, I've taught university students who now work at Google and Facebook, I've held live trainings for software engineering teams at Adobe and Apple, and I'm now so excited to share what I know with a wider community online. | Scala | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
MySQL - Statistics for Data Science & Business Analytics | SQL - MySQL for Data Analytics - Beginners - Statistics for Data Science - MySQL for Data Analysis - with 25 projects | 4.3 | 542 | 7153 | Created by Mahmoud Ali | Feb-22 | English | $9.99 | 14h 26m total length | https://www.udemy.com/course/ureadup_sds/ | Mahmoud Ali | System analyst , Consultant , AI engineer | 4.3 | 542 | 7153 | 300+ Lectures/Articles lectures include real life practical projects and examples for people need to learn SQL - MySQL for data science and statistics Enjoy practice 25 Apps/Projects in SQL - MySQL for data analytics and learn Statistics for data science . This is the Complete 2021 Bootcamp , Two courses in ONE COURSE . Do you like jobs in MySQL for data science ? Do you like jobs in Machine learning ? Do you like jobs in Marketing analyst ? Do you like jobs in Programming for data science ? Do you like jobs in Business analysis and business intelligence ? If the answer is yes , then all of the above needs MySQL and Statistics for data science . Who prepared this course material ? This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and google . After hard working from five months ago we created 300+ Lectures/Articles to cover everything related to SQL - MySQL & statistics for data science . In no time with simple and easy way you will learn and love SQL for data science and statistics . We stress in this course to make it very spontaneous to make all students love SQL and statistics for data science . Who's teaching you in this course ? I'm senior developer and chief data science engineer . i worked for many projects related to expert systems and artificial intelligence . Also i worked as Tutor and consultant trainer with a leader international companies located in USA and UK . I spent over five months of hard working to create 300+ Lectures/Articles in super high quality to make all students enjoy and love SQL for data science and statistics. I'm sharing a lot of practical experience from my own work with you in this course . Why learn MySQL or SQL ? MySQL is a popular database platform for businesses because it is extremely easy to use. It is commonly used in combination with web development and data science . You hear “it’s easy to work with” a lot in relation to computer languages, but MySQL truly is simple. For instance, someone with little to no knowledge of MySQL can easily establish a database . Of course, a lot of hosting providers make this process even simpler by handling all the necessary tasks for new website administrators, but it doesn’t detract from the point that MySQL is relatively easy to use. I could not imagine data science without databases . What is my final goal after my students enroll in this course ? My final goal is to make all students and engineers love SQL for data science and statistics . My big challenge in this course is to make it professional course at the same time it should be very easy and simple for all People . Therefore you will notice that i used a lot of graphics and imaginary ideas to make you LOVE SQL for data science and statistics So, what are you waiting for? Click the “Take this course” button, and let’s begin this journey together! What is course contents ? Starting introduction to data science and data analysis . Understand programming basics for data science learn SQL - MySQL basics . Write all the SQL joins Create Foreign key and primary key in MySQL databases . Learn how to start and stop MySQL server . Analyze data using Aggregate Functions . Read and import external CSV files into MySQL database . Export MySQL database table contents into CSV file . Awesome Projects and examples like : How Mr. Genie helped us to find all fishes in the sea ? Mr. Genie power versus statistics power The double edged sword of statistics Help fisherman to catch Tuna using sampling distribution Ice Cola example with student's t distribution . Estimation of goals in premier league ( using confidence interval ) . TAKE YOUR BREATH BEFORE HYPOTHESIS TESTING One sample mean t test . Two sample mean t test . Null hypothesis and alternative hypothesis . What is P value ? How to calculate P value using manual and direct method ? Two mini stories for TWO PROJECTS related to hypothesis testing Project one is how Sarah used "one sample mean t test" for Ice Cola factory to prove that her brother Ibrahim is innocent ? Project two how Sarah used "two sample mean t test" for Ice Cola factory to help her brother Ibrahim to increase Ice Cola sales in winter ? ... more and more .... and more and more course contents #SQL #MySQL #MySQL-for-data-science #SQL-for-Data-science | https://www.udemy.com/course/ureadup_sds/#instructor-1 | My name is Aly and i'm very happy that you are reading this! Professionally, I come from Artificial intelligence world with experience in mobility , medical , retail and automotive . I learned and was trained by the professional professors in Munich , Germany , then i decided to share what i learned from my professors by creating courses at Udemy From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Business Analyst | Consultant | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||
Apache Spark Hands on Specialization for Big Data Analytics | In-depth course to master Apache Spark Development using Scala for Big Data (with 30+ real-world & hands-on examples) | 4 | 537 | 12370 | Created by Irfan Elahi | Aug-19 | English | $9.99 | 11h 50m total length | https://www.udemy.com/course/apache-spark-hands-on-course-big-data-analytics/ | Irfan Elahi | Data Scientist in the world's largest consultancy firm | 4.2 | 1070 | 30684 | What if you could catapult your career in one of the most lucrative domains i.e. Big Data by learning the state of the art Hadoop technology (Apache Spark) which is considered mandatory in all of the current jobs in this industry? What if you could develop your skill-set in one of the most hottest Big Data technology i.e. Apache Spark by learning in one of the most comprehensive course out there (with 10+ hours of content) packed with dozens of hands-on real world examples, use-cases, challenges and best-practices? What if you could learn from an instructor who is working in the world's largest consultancy firm, has worked, end-to-end, in Australia's biggest Big Data projects to date and who has a proven track record on Udemy with highly positive reviews and thousands of students already enrolled in his previous course(s)? If you have such aspirations and goals, then this course and you is a perfect match made in heaven! Why Apache Spark? Apache Spark has revolutionised and disrupted the way big data processing and machine learning were done by virtue of its unprecedented in-memory and optimised computational model. It has been unanimously hailed as the future of Big Data. It's the tool of choice all around the world which allows data scientists, engineers and developers to acquire and process data for a number of use-cases like scalable machine learning, stream processing and graph analytics to name a few. All of the leading organisations like Amazon, Ebay, Yahoo among many others have embraced this technology to address their Big Data processing requirements. Additionally, Gartner has repeatedly highlighted Apache Spark as a leader in Data Science platforms. Certification programs of Hadoop vendors like Cloudera and Hortonworks, which have high esteem in current industry, have oriented their curriculum to focus heavily on Apache Spark. Almost all of the jobs in Big Data and Machine Learning space demand proficiency in Apache Spark. This is what John Tripier, Alliances and Ecosystem Lead at Databricks has to say, “The adoption of Apache Spark by businesses large and small is growing at an incredible rate across a wide range of industries, and the demand for developers with certified expertise is quickly following suit”. All of these facts correlate to the notion that learning this amazing technology will give you a strong competitive edge in your career. Why this course? Firstly, this is the most comprehensive and in-depth course ever produced on Apache Spark. I've carefully and critically surveyed all of the resources out there and almost all of them fail to cover this technology in the depth that it truly deserves. Some of them lack coverage of Apache Spark's theoretical concepts like its architecture and how it works in conjunction with Hadoop, some fall short in thoroughly describing how to use Apache Spark APIs optimally for complex big data problems, some ignore the hands-on aspects to demonstrate how to do Apache Spark programming to work on real-world use-cases and almost all of them don't cover the best practices in industry and the mistakes that many professionals make in field. This course addresses all of the limitations that's prevalent in the currently available courses. Apart from that, as I have attended trainings from leading Big Data vendors like Cloudera (for which they charge thousands of dollars), I've ensured that the course is aligned with the educational patterns and best practices followed in those training to ensure that you get the best and most effective learning experience. Each section of the course covers concepts in extensive detail and from scratch so that you won't find any challenges in learning even if you are new to this domain. Also, each section will have an accompanying assignment section where we will work together on a number of real-world challenges and use-cases employing real-world data-sets. The data-sets themselves will also belong to different niches ranging from retail, web server logs, telecommunication and some of them will also be from Kaggle (world's leading Data Science competition platform). The course leverages Scala instead of Python. Even though wherever possible, reference to Python development is also given but the course is majorly based on Scala. The decision was made based on a number of rational factors. Scala is the de-facto language for development in Apache Spark. Apache Spark itself is developed in Scala and as a result all of the new features are initially made available in Scala and then in other languages like Python. Additionally, there is significant performance difference when it comes to using Apache Spark with Scala compared to Python. Scala itself is one of the most highest paid programming languages and you will be developing strong skill in that language along the way as well. The course also has a number of quizzes to further test your skills. For further support, you can always ask questions to which you will get prompt response. I will also be sharing best practices and tips on regular basis with my students. What you are going to learn in this course? The course consists of majorly two sections: Section - 1: We'll start off with the introduction of Apache Spark and will understand its potential and business use-cases in the context of overall Hadoop ecosystem. We'll then focus on how Apache Spark actually works and will take a deep dive of the architectural components of Spark as its crucial for thorough understanding. Section - 2: After developing understanding of Spark architecture, we will move to the next section of this course where we will employ Scala language to use Apache Spark APIs to develop distributed computation programs. Please note that you don't need to have prior knowledge of Scala for this course as I will start with the very basics of Scala and as a result you will also be developing your skills in this one of the highest paying programming languages. In this section, We will comprehensively understand how spark performs distributed computation using abstractions like RDDs, what are the caveats in loading data in Apache Spark, what are the different ways to create RDDs and how to leverage parallelism and much more. Furthermore, as transformations and action constitute the gist of Apache Spark APIs thus its imperative to have sound understanding of these. Thus, we will then focus on a number of Spark transformations and Actions that are heavily being used in Industry and will go into detail of each. Each API usage will be complimented with a series of real-world examples and datasets e.g. retail, web server logs, customer churn and also from kaggle. Each section of the course will have a number of assignments where you will be able to practically apply the learned concepts to further consolidate your skills. A significant section of the course will also be dedicated to key value RDDs which form the basis of working optimally on a number of big data problems. In addition to covering the crux of Spark APIs, I will also highlight a number of valuable best practices based on my experience and exposure and will also intuit on mistakes that many people do in field. You will rarely such information anywhere else. Each topic will be covered in a lot of detail with strong emphasis on being hands-on thus ensuring that you learn Apache Spark in the best possible way. The course is applicable and valid for all versions of Spark i.e. 1.6 and 2.0. After completing this course, you will develop a strong foundation and extended skill-set to use Spark on complex big data processing tasks. Big data is one of the most lucractive career domains where data engineers claim salaries in high numbers. This course will also substantially help in your job interviews. Also, if you are looking to excel further in your big data career, by passing Hadoop certifications like of Cloudera and Hortonworks, this course will prove to be extremely helpful in that context as well. Lastly, once enrolled, you will have life-time access to the lectures and resources. Its a self-paced course and you can watch lecture videos on any device like smartphone or laptop. Also, you are backed by Udemy's rock-solid 30 days money back guarantee. So if you are serious about learning about learning Apache Spark, enrol in this course now and lets start this amazing journey together! | https://www.udemy.com/course/apache-spark-hands-on-course-big-data-analytics/#instructor-1 | A full stack scalable analytics specialist, working in the world's largest consultancy firm in Australia, with a growing portfolio of successful projects delivering substantial impact and value in multiple capacities across telecom, retail, energy and health-care sectors. Additionally: • Artificial Intelligence (AI) stream-lead in Deloitte Australia's Azure Enablement Initiative • Member of Deloitte Australia's ClearLight initiative managing AWS and Azure platform for enablement and assets prototyping • Trainer of Deloitte's internal Data Science training program • Author of "Scala Programming for Big Data Analytics" book published by Apress • Technical reviewer of "Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark" book published by Apress • Instructor of Apache Spark and R Programming courses on Udemy with thousands of students enrolled from all around the world • Designated author of the largest Data Science publication (Towards Data Science) on Medium • Speaker at DataWorks Summit in 2017 in Sydney on in-memory Big Data Technologies • Speaker in Data Analytics Explained meetup and in multiple universities all around the world | Data Analyst | Data Scientist | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
R Programming Hands-on Specialization for Data Science (Lv1) | An in-depth course on R language with real-world Data Science examples to supercharge your R data analysis skills | 4.4 | 533 | 21443 | Created by Irfan Elahi | May-17 | English | $9.99 | 10h 59m total length | https://www.udemy.com/course/r-programming-data-science-hands-on-course/ | Irfan Elahi | Data Scientist in the world's largest consultancy firm | 4.2 | 1070 | 30684 | R is considered as lingua franca of Data Science. Candidates with expertise in R programming language are in exceedingly high demand and paid lucratively in Data Science. IEEE has repeatedly ranked R as one of the top and most popular Programming Languages. Almost every Data Science and Machine Learning job posted globally mentions the requirement for R language proficiency. All the top ranked universities like MIT have included R in their respective Data Science courses curriculum. With its growing community of users in Open Source space, R allows you to productively work on complex Data Analysis and Data Science projects to acquire, transform/cleanse, analyse, model and visualise data to support informed decision making. But there's one catch: R has quite a steep learning curve! How's this course different from so many other courses? Many of the available training courses on R programming don't cover it its entirety. To be proficient in R for Data Science requires thorough understanding of R programming constructs, data structures and none of the available courses cover them with the comprehensiveness and depth that each topic deserves. Many courses dive straight into Machine Learning algorithms and advanced stuff without thoroughly comprehending the R programming constructs. Such approaches to teach R have a lot of drawbacks and that's where many Data Scientists struggle with in their professional careers. Also, the real value in learning R lies in learning from professionals who are experienced, proficient and are still working in Industry on huge projects; a trait which is missing in 90% of the training courses available on Udemy and other platforms. This is what makes this course stand-out from the rest. This course has been designed to address these and many other fallacies and uniquely teaches R in a way that you won't find anywhere else. Taught by me, an experienced Data Scientist currently working in Deloitte (World's largest consultancy firm) in Australia and has worked on a number of Data Science projects in multiple niches like Retail, Web, Telecommunication and web-sector. I have over 5 years of diverse experience of working in my own start-ups (related to Data Science and Networking), BPO and digital media consultancy firms, and in academia's Data Science Research Labs. Its a rare combination of exposure that you will hardly find in any other instructor. You will be leveraging my valuable experience to learn and specialize R. What you're going to learn in this course? The course will start from the very basics of introducing Data Science, importance of R and then will gradually build your concepts. In the first segment, we'll start from setting up R development environment, R Data types, Data Structures (the building blocks of R scripts), Control Structures and Functions. The second segment comprises of applying your learned skills on developing industry-grade Data Science Application. You will be introduced to the mind-set and thought-process of working on Data Science Projects and Application development. The project will then focus on developing automated and robust Web Scraping bot in R. You will get the amazing opportunities to discover what multiple approaches are available and how to assess alternatives while making design decisions (something that Data Scientists do everyday). You will also be exposed to web technologies like HTML, Document Object Model, XPath, RSelenium in the context of web scraping, that will take your data analysis skills to the next level. The course will walk you through the step by step process of scraping real-life and live data from a classifieds website to analyse real-estate trends in Australia. This will involve acquiring, cleansing, munging and analyzing data using R statistical and visualisation capabilities. Each and every topic will be thoroughly explained with real-life hands-on examples, exercises along with disseminating implications, nuances, challenges and best-practices based on my years of experience. What you will gain from this course will be incomparable to what's currently available out there as you will be leveraging my growing experience and exposure in Data Science. This course will position you to not only apply for Data Science jobs but will also enable you to use R for more challenging industry-grade projects/problems and ultimately it will super-charge your career. So take the decision and enrol in this course and lets work together to make you specialize in R Programming like never before! | https://www.udemy.com/course/r-programming-data-science-hands-on-course/#instructor-1 | A full stack scalable analytics specialist, working in the world's largest consultancy firm in Australia, with a growing portfolio of successful projects delivering substantial impact and value in multiple capacities across telecom, retail, energy and health-care sectors. Additionally: • Artificial Intelligence (AI) stream-lead in Deloitte Australia's Azure Enablement Initiative • Member of Deloitte Australia's ClearLight initiative managing AWS and Azure platform for enablement and assets prototyping • Trainer of Deloitte's internal Data Science training program • Author of "Scala Programming for Big Data Analytics" book published by Apress • Technical reviewer of "Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark" book published by Apress • Instructor of Apache Spark and R Programming courses on Udemy with thousands of students enrolled from all around the world • Designated author of the largest Data Science publication (Towards Data Science) on Medium • Speaker at DataWorks Summit in 2017 in Sydney on in-memory Big Data Technologies • Speaker in Data Analytics Explained meetup and in multiple universities all around the world | Misc | Data Scientist | >=4 | Below 1K | >=20K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Machine Learning with Imbalanced Data | Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning. | 4.8 | 525 | 5875 | Created by Soledad Galli | Jun-22 | English | $10.99 | 11h 15m total length | https://www.udemy.com/course/machine-learning-with-imbalanced-data/ | Soledad Galli | Lead Data Scientist | 4.5 | 10170 | 46124 | Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models. If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn: Under-sampling methods at random or focused on highlighting certain sample populations Over-sampling methods at random and those which create new examples based of existing observations Ensemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performance Cost sensitive methods which penalize wrong decisions more severely for minority classes The appropriate metrics to evaluate model performance on imbalanced datasets By the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets. This comprehensive machine learning course includes over 50 lectures spanning more than 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects. In addition, the code is updated regularly to keep up with new trends and new Python library releases. So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models. | https://www.udemy.com/course/machine-learning-with-imbalanced-data/#instructor-1 | Soledad Galli is a lead data scientist and founder of Train in Data. She has experience in finance and insurance, received a Data Science Leaders Award in 2018 and was selected “LinkedIn’s voice” in data science and analytics in 2019. Sole is passionate about sharing knowledge and helping others succeed in data science. As a data scientist in Finance and Insurance companies, Sole researched, developed and put in production machine learning models to assess Credit Risk, Insurance Claims and to prevent Fraud, leading in the adoption of machine learning in the organizations. Sole is passionate about empowering people to step into and excel in data science. She mentors data scientists, writes articles online, speaks at data science meetings, and teaches online courses on machine learning. Sole has recently created Train In Data, with the mission to facilitate and empower people and organizations worldwide to step into and excel in data science and analytics. Sole has an MSc in Biology, a PhD in Biochemistry and 8+ years of experience as a research scientist in well-known institutions like University College London and the Max Planck Institute. She has scientific publications in various fields such as Cancer Research and Neuroscience, and her research was covered by the media on multiple occasions. Soledad has 4+ years of experience as an instructor in Biochemistry at the University of Buenos Aires, taught seminars and tutorials at University College London, and mentored MSc and PhD students at Universities. Feel free to contact her on LinkedIn. ======================== Soledad Galli es científica de datos y fundadora de Train in Data. Tiene experiencia en finanzas y seguros, recibió el premio Data Science Leaders Award en 2018 y fue seleccionada como "la voz de LinkedIn" en ciencia y análisis de datos en 2019. A Soledad le apasiona compartir conocimientos y ayudar a otros a tener éxito en la ciencia de datos. Como científica de datos en compañías de finanzas y seguros, Sole desarrolló y puso en producción modelos de aprendizaje automático para evaluar el riesgo crediticio, automatizar reclamos de seguros y para prevenir el fraude, facilitando la adopción del aprendizaje de máquina en estas organizaciones. A Sole le apasiona ayudar a que las personas aprendan y se destaquen en ciencia de datos, es por eso habla regularmente en reuniones de ciencia de datos, escribe varios artículos disponibles en la web y crea cursos sobre aprendizaje de máquina. Sole ha creado recientemente Train In Data, con la misión de ayudar a las personas y organizaciones de todo el mundo a que aprendan y se destaquen en la ciencia y análisis de datos. Sole tiene una maestría en biología, un doctorado en bioquímica y más de 8 años de experiencia como investigadora científica en instituciones prestigiosas como University College London y el Instituto Max Planck. Tiene publicaciones científicas en diversos campos, como la investigación contra el Cáncer y la Neurociencia, y sus resultados fueron cubiertos por los medios en múltiples ocasiones. Soledad tiene más de 4 años de experiencia como instructora de bioquímica en la Universidad de Buenos Aires, dio seminarios y tutoriales en University College London, en Londres, y fue mentora de estudiantes de maestría y doctorado en diferentes universidades. No dudes en contactarla en LinkedIn. | Machine Learning | Chief/Lead Role | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Web Scraping in Python With BeautifulSoup and Selenium 2022 | The most up to date and project based Web Scraping course in Python using BeautifulSoup and Selenium! | Bestseller | 4.6 | 523 | 3133 | Created by Christopher Zita | Nov-22 | English | $9.99 | 9h 28m total length | https://www.udemy.com/course/web-scraping-in-python-with-beautifulsoup-and-selenium/ | Christopher Zita | Data Analyst for Team Canada | 4.6 | 523 | 3133 | Web Scraping has become one of the hottest topics in the data science world, for getting access to data can make or break you. This is why Fortune 500 companies like Walmart, CNN, Target, and Amazon use web scraping to get ahead and stay ahead with data. It’s the original growth tool and one of their best-kept secrets …And it can easily be yours too. Welcome to Web Scraping in Python with BeautiuflSoup and Selenium! The most up to date and project-oriented course out there currently. In this course, you're going to learn how to scrape data off some of the most well-known websites which include: Twitter Airbnb Nike Google Indeed NFL MarketWatch Worldometers IMDb Carpages At the end of this course, you will understand the most important components of web scraping and be able to build your own web scrapers to obtain new data from any website, automate any task using web scraping, and more. Plus, familiarize yourself with some of the most common scraping techniques and sharpen your Python programming skills while you’re at it! First, learn the essentials of web scraping, explore the framework of a website, and get your local environment ready to take on scraping challenges with BeautifulSoup, and Selenium. Next, cover the basics of BeautifulSoup, utilize the requests library and LXML parser, and scale up to deploy a new scraping algorithm to scrape data from any table online, and from multiple pages. Third, set up Selenium to deal with JavaScript-driven webpages, and use the unique functions of Selenium to interact with pages. Combine the concepts of BeautifulSoup and Selenium to create the most effective scrapers to deal with some of the most challenging websites. Finally, learn how to make web scraping fully automatic by running your scraper at a specific time each day. What makes this course different from the others, and why you should enroll? First, this is the most updated course currently out Second, this is the most project-based course you will find, where we will scrape many of the internets most well-known websites You will have an in-depth step by step guide on how to become a professional web scraper. You will learn how to use Selenium to scrape JavaScript websites and I can assure you, you won't find any tutorials out there that teach you how to really use Selenium like I'll be doing in this course. You will learn how to create a fully automated web scraping script that runs periodically without any intervention from you. 30 days money-back guarantee by Udemy So whether you’re a data scientist, machine learning, or AI engineer who wants to access more data sources; a web developer looking to automate tasks, or a data buff with a general interest in data science and web scraping… This course delivers an in-depth presentation of web scraping basics, methodologies, and approaches that you can easily apply to your own personal projects, or out there in the real world of business. Join me now and let’s start scraping the web together. Enroll today. | https://www.udemy.com/course/web-scraping-in-python-with-beautifulsoup-and-selenium/#instructor-1 | I am a Data Science enthusiast who loves everything from Machine Learning to Computer Vision to Web Scraping. I have done many online courses and taken all the best parts of other courses and put them into my own. For work, I am a Data Scientist that helps Team Canada use data to their advantage in order to obtain medals in the next Olympics. | Python | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
2022 Natural Language Processing in Python for Beginners | Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing | 4.3 | 522 | 5470 | Created by Laxmi Kant | Nov-22 | English | $9.99 | 29h 56m total length | https://www.udemy.com/course/nlp-in-python/ | Laxmi Kant | Principal Data Scientist at mBreath and KGPTalkie | 4.4 | 1946 | 46559 | Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge-cutting NLP problems. You should have an introductory knowledge of Python and Machine Learning before enrolling in this course otherwise please do not enroll in this course. In this course, we will start from level 0 to the advanced level. We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing. In the next section, we will learn about working with Files for storing and loading the text data. This section is the foundation of another section on Complete Text Preprocessing. I will show you many ways of text preprocessing using Spacy and Regular Expressions. Finally, I will show you how you can create your own python package on preprocessing. It will help us to improve our code writing skills. We will be able to reuse our code systemwide without writing codes for preprocessing every time. This section is the most important section. Then, we will start the Machine learning theory section and a walkthrough of the Scikit-Learn Python package where we will learn how to write clean ML code. Thereafter, we will develop our first text classifier for SPAM and HAM message classification. I will be also showing you various types of word embeddings used in NLP like Bag of Words, Term Frequency, IDF, and TF-IDF. I will show you how you can estimate these features from scratch as well as with the help of the Scikit-Learn package. Thereafter we will learn about the machine learning model deployment. We will also learn various other important tools like word2vec, GloVe, Deep Learning, CNN, LSTM, RNN, etc. At the end of this lesson, you will learn everything which you need to solve your own NLP problem. | https://www.udemy.com/course/nlp-in-python/#instructor-1 | I am a Principal Data Scientist at SleepDoc and a Ph.D. in Data Science from the Indian Institute of Technology (IIT). I had also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNC. I have also taught programming at IIT for few years and then later started a YouTube channel, KGP Talkie with 20K+ subscribers. I am very well connected with industry and academia. | NLP | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Data Science and Machine Learning For Beginners with Python | Learn to Analyse , Make Predictions, Explore data Frames,Clean and Visualize Data | 3.8 | 521 | 30036 | Created by Bluelime Learning Solutions | Jun-21 | English | $9.99 | 7h 55m total length | https://www.udemy.com/course/data-science-and-machine-learning-for-beginners-with-python-c/ | Bluelime Learning Solutions | Learning made simple | 4.1 | 36082 | 742296 | Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information . Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development. Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights. Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. We will also be using SQL to interact with data inside a PostgreSQL Database. What you'll learn Understand Data Science Life Cycle Use Kaggle Data Sets Perform Probability Sampling Explore and use Tabular Data Explore Pandas DataFrame Manipulate Pandas DataFrame Perform Data Cleaning Perform Data Visualization Visualize Qualitative Data Explore Machine Learning Frameworks Understand Supervised Machine Learning Use machine learning to predict value of a house Use Scikit-Learn Load datasets Make Predictions using machine learning Understand Python Expressions and Statements Understand Python Data Types and how to cast data types Understand Python Variables and Data Structures Understand Python Conditional Flow and Functions Learn SQL with PostgreSQL Perform SQL CRUD Operations on PostgreSQL Database Filter and Sort Data using SQL Understand Big Data Terminologies A Data Scientist can work as the following: data analyst. machine learning engineer. business analyst. data engineer. IT system analyst. data analytics consultant. digital marketing manager. | https://www.udemy.com/course/data-science-and-machine-learning-for-beginners-with-python-c/#instructor-1 | Bluelime is UK based and creates quality easy to understand eLearning solutions .All our courses are 100% video based. We teach hands –on- examples that teach real life skills . Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need. | Machine Learning | >=3 | Below 1K | >=30K | >=4 | Below 1 Lakh | >=5 Lakh | ||||||||||||||||||
Regression Analysis for Statistics & Machine Learning in R | Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R | 4.3 | 513 | 4338 | Created by Minerva Singh | Oct-22 | English | $11.99 | 7h 39m total length | https://www.udemy.com/course/regression-analysis-for-statistics-machine-learning-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | With so many R Statistics & Machine Learning courses around, why enrol for this? Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher. My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. You will go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. Become a Regression Analysis Expert and Harness the Power of R for Your Analysis Get started with R and RStudio. Install these on your system, learn to load packages and read in different types of data in R Carry out data cleaning and data visualization using R Implement ordinary least square (OLS) regression in R and learn how to interpret the results. Learn how to deal with multicollinearity both through variable selection and regularization techniques such as ridge regression Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods. Evaluate regression model accuracy Implement generalized linear models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices. Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data. Work with tree-based machine learning models Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy. Carry out model selection Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in a renowned international journal like PLOS One. Specifically, the course will: (a) Take the students with a basic level of statistical knowledge to perform some of the most common advanced regression analysis based techniques (b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks (c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation (d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. | https://www.udemy.com/course/regression-analysis-for-statistics-machine-learning-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Machine Learning | Data Scientist | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
Python for Machine Learning: The Complete Beginner's Course | Learn to create machine learning algorithms in Python for students and professionals | 4.2 | 511 | 63263 | Created by Meta Brains | May-22 | English | $9.99 | 2h 28m total length | https://www.udemy.com/course/python-for-machine-learning-beginners/ | Meta Brains | Let's code & build the metaverse together! | 4.2 | 7800 | 319977 | To understand how organizations like Google, Amazon, and even Udemy use machine learning and artificial intelligence (AI) to extract meaning and insights from enormous data sets, this machine learning course will provide you with the essentials. According to Glassdoor and Indeed, data scientists earn an average income of $120,000, and that is just the norm! When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find. Like the Wall Street "quants" of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods. That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years. The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist. In this course, mathematical notations and jargon are minimized, each topic is explained in simple English, making it easier to understand. Once you've gotten your hands on the code, you'll be able to play with it and build on it. The emphasis of this course is on understanding and using these algorithms in the real world, not in a theoretical or academic context. You'll walk away from each video with a fresh idea that you can put to use right away! All skill levels are welcome in this course, and even if you have no prior statistical experience, you will be able to succeed! | https://www.udemy.com/course/python-for-machine-learning-beginners/#instructor-1 | Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for Coding, Finance & Excel. We bring together both professional and educational experiences to create world-class training programs accessible to everyone. Currently, we're focused on the next great revolution in computing: The Metaverse. Our ultimate objective is to train the next generation of talent so we can code & build the metaverse together! | Machine Learning | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Machine Learning No-Code Approach: Using Azure ML Studio | A hands-on approach to Machine Learning using the easy drag-n-drop environment of Azure Machine Learning Studio | Bestseller | 4.8 | 511 | 5490 | Created by Aderson Oliveira, Software Architect.ca, Scott Duffy • 800.000+ Students | Sep-21 | English | $12.99 | 2h 40m total length | https://www.udemy.com/course/machine-learning-no-code-approach-using-azure-ml-studio/ | Aderson Oliveira | Tech Instructor | 4.8 | 511 | 5490 | Machine Learning is the most in demand technical skill in today's business environment. Most of the time though it is reserved for professionals that know how to code. But Microsoft Azure Machine Learning Studio changed that. It brings a drag-n-drop easy to use environment to anyone’s fingertips. Microsoft is known for its easy-of-use tools and Azure ML Studio is no different. However, as easy as Azure ML Studio is, if you don’t know Machine Learning, at least the basics, you won’t be able to do much with the tool. This is one of the goals of this course: To give you the foundational understanding about Machine Learning. You will get the base knowledge required to not only talk proficiently about ML, but also to put it into action and execute on business needs. We will go through all the steps necessary to put together a Supervised Learning prediction model, whether you need Classification (for discrete values like “Approved” or “Nor Approved”) or Regression (for continuous values like “Salary” or “Price”). The course will only require you to have basic knowledge of math including the basic operations and how to calculate average. Some exposure to Microsoft Excel would be good as during deployment of the live model, we will be using Excel to perform demonstrations. This course has been designed keeping in mind technologists with no coding background as we use a “no-code approach”. It is very hands-on, and you will be able to develop your own models while learning. We will cover: - Basics of the main three main types of Machine Learning Algorithms - Supervised Learning in depth - Classification by using the Titanic Dataset - Understanding and selecting the features from the dataset - Changing the metadata of features to work better with ML Algorithms - Splitting the data - Selecting the Algorithm - Training, scoring, and evaluating the model - Regression by using the Melbourne Real Estate Dataset - Cleaning missing data - Stratifying the data - Tuning hyperparameters - Deploying the models to a Excel - Providing web service details to developers in case you want to integrate with external systems - Azure ML Cheat Sheet The course also includes 4 assignments with solutions that will give you an extra chance to practice your newly acquired Machine Learning skills. In the end you will be able to use your own datasets to help your company with data prediction or, if you just want to impress the boss, you will be able to show the new tool you have just added to your toolbelt. If you are not a coder and thought there would be no place for you to ride the Machine Learning wave, think again. You can not only be part of it, but you can master it and become a Machine Learning hero with Azure ML Studio. Enroll today and I will see you inside! | https://www.udemy.com/course/machine-learning-no-code-approach-using-azure-ml-studio/#instructor-1 | My name is Aderson Oliveira and I love to teach technology. I have always enjoyed guiding people through hard to understand technical topics, but that became more evident back in 2018 when I started teaching coding in a post-secondary environment. I love the "Aha!" moments that I help to create with my students when they finally get it. I have been working as a developer for the past 20 years and in the last 10 years I have been working as a consultant, running my own service helpdesk business. I have mostly focused on Microsoft related technologies like Azure, C#, SQL Server and .NET Core Framework. I have been teaching online since 2010 and more recently in the past 4 years I have been focusing on AI and Machine Learning. I enjoy learning and I'm here on Udemy to share what I know with anyone interested to join the learning journey with me. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||
Regression, Data Mining, Text Mining, Forecasting using R | Learn Regression Techniques, Data Mining, Forecasting, Text Mining using R | 4 | 509 | 4482 | Created by ExcelR Solutions | Aug-18 | English | $12.99 | 32h 56m total length | https://www.udemy.com/course/data-science-using-r/ | ExcelR Solutions | Pioneer in professional management trainings & consulting | 3.9 | 1826 | 15117 | Data Science using R is designed to cover majority of the capabilities of R from Analytics & Data Science perspective, which includes the following: Learn about the basic statistics, including measures of central tendency, dispersion, skewness, kurtosis, graphical representation, probability, probability distribution, etc.Learn about scatter diagram, correlation coefficient, confidence interval, Z distribution & t distribution, which are all required for Linear Regression understandingLearn about the usage of R for building Regression models Learn about the K-Means clustering algorithm & how to use R to accomplish the sameLearn about the science behind text mining, word cloud, sentiment analysis & accomplish the same using RLearn about Forecasting models including AR, MA, ES, ARMA, ARIMA, etc., and how to accomplish the same using RLearn about Logistic Regression & how to accomplish the same using R | https://www.udemy.com/course/data-science-using-r/#instructor-1 | Certifications: Certified Six Sigma Master Black Belt Project Management Professional (PMP) Agile Certified Practitioner (PMI - ACP) Risk Management Professional (PMI-RMP) Certified Scrum Master Agile Project Management – Foundation & Practitioner from APMG Bharani Kumar is an Alumnus of premier institutions like IIT & ISB with 15+ years professional experience and worked in various MNCs such as HSBC, ITC, Infosys, Deloitte in various capacities such as Data Scientist, Project Manager, Service Delivery Manager, Process Consultant, Delivery Head etc. He has trained over 1500 professionals across the globe on Business Analytics, Agile, PMP, Lean Six Sigma, Business analytics and the likes. He has 8 years of extensive experience in corporate, open house and online training. He is a thorough implementer with abilities in Business Analytics and Agile projects. He worked in Delivery management focusing on maximizing business value articulation. He has a comprehensive experience in leading teams and multiple projects. Quality Management: A thorough implementer with abilities in Quality management focusing on maximizing customer satisfaction, process compliance and business value articulation; comprehensive experience in leading teams & multiple projects. A result-oriented leader with expertise in devising strategies aimed at enhancing overall organizational growth, sustained profitability of operations and improved business performance. Project Management: Project Management Professional involved in Initiation, Planning, Execution, Monitoring & Controlling and Closing phases of project activities. Devising and implementing project plans within preset budgets and deadlines and managing the projects towards successful delivery of project deliverables and meeting project objectives. Training: Close to 8 years training experience and conducted multiple trainings in PMP, Agile, Six Sigma, Business Analytics and Process Excellence across the globe. Understands the individual differences of the attendees and possesses excellent training skills and considered as one of the best trainers in his areas of expertise. | Misc | >=4 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Data Science Masterclass With R 8 Case Studies + 4 Projects | Data Science by IITian -Data Science+R Programming ,Data analysis, Data Visualization, Data Pre-processing etc | 4.7 | 507 | 6771 | Created by Up Degree | Aug-19 | English | $199.99 | 32h 2m total length | https://www.udemy.com/course/data-science-complete-course/ | Up Degree | New Skills Everyday! | 3.8 | 4671 | 84324 | Are you planing to build your career in Data Science in This Year? Do you the the Average Salary of a Data Scientist is $100,000/yr? Do you know over 10 Million+ New Job will be created for the Data Science Filed in Just Next 3 years?? If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science! Do you Ever Wonder that Data Science is the "Hottest" Job Globally in 2018 - 2019! >> 30+ Hours Video >> 4 Capstone Projects >> 8+ Case Studies >> 24x7 Support >>ENROLL TODAY & GET DATA SCIENCE INTERVIEW PREPARATION COURSE FOR FREE << What Projects We are Going to Cover In the Course? Project 1- Titanic Case Study which is based on Classification Problem. Project 2 - E-commerce Sale Data Analysis - based on Regression. Project 3 - Customer Segmentation which is based on Unsupervised learning. Final Project - Market Basket Analysis - based on Association rule mining Why Data Science is a MUST HAVE for Now A Days? The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning! What Students Are Saying: "A great course to kick-start journey in Machine Learning. It gives a clear contextual overview in most areas of Machine Learning . The effort in explaining the intuition of algorithms is especially useful" - John Doe, Co-Founder, Impressive LLC I simply love this course and I definitely learned a ton of new concepts. Nevertheless, I wish there was some real life examples at the end of the course. A few homework problems and solutions would’ve been good enough. - - Brain Dee, Data Scientist It was amazing experience. I really liked the course. The way the trainers explained the concepts were too good. The only think which I thought was missing was more of real world datasets and application in the course. Overall it was great experience. The course will really help the beginners to gain knowledge. Cheers to the team - - Devon Smeeth, Software Developer Above, we just give you a very few examples why you Should move into Data Science and Test the Hot Demanding Job Market Ever Created! The Good News is That From this Hands On Data Science and Machine Learning in R course You will Learn All the Knowledge what you need to be a MASTER in Data Science. Why Data Science is a MUST HAVE for Now A Days? The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning! Here we list a Very Few Companies : - Google - For Advertise Serving, Advertise Targeting, Self Driving Car, Super Computer, Google Home etc. Google use Data Science + ML + AI to Take Decision Apple: Apple Use Data Science in different places like: Siri, Face Detection etc Facebook: Data Science , Machine Learning and AI used in Graph Algorithm for Find a Friend, Photo Tagging, Advertising Targeting, Chat bot, Face Detection etc NASA: Use Data Science For different Purpose Microsoft: Amplifying human ingenuity with Data Science So From the List of the Companies you can Understand all Big Giant to Very Small Startups all are chessing Data Science and Artificial Intelligence and it the Opportunity for You! Why Choose This Data Science with R Course? We not only "How" to do it but also Cover "WHY" to do it? Theory explained by Hands On Example! 30+ Hours Long Data Science Course 100+ Study Materials on Each and Every Topic of Data Science! Code Templates are Ready to Download! Save a lot of Time What You Will Learn From The Data Science MASTERCLASS Course: Learn what is Data science and how Data Science is helping the modern world! What are the benefits of Data Science , Machine Learning and Artificial Intelligence Able to Solve Data Science Related Problem with the Help of R Programming Why R is a Must Have for Data Science , AI and Machine Learning! Right Guidance of the Path if You want to be a Data Scientist + Data Science Interview Preparation Guide How to switch career in Data Science? R Data Structure - Matrix, Array, Data Frame, Factor, List Work with R’s conditional statements, functions, and loops Systematically explore data in R Data Science Package: Dplyr , GGPlot 2 Index, slice, and Subset Data Get your data in and out of R - CSV, Excel, Database, Web, Text Data Data Science - Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot Data Science - Data Manipulation - Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R Statistics - A Must have for Data Science Data Science - Hypothesis Testing Business Use Case Understanding Data Pre-processing Supervised Learning Logistic Regression K-NN SVM Naive Bayes Decision Tree Random Forest K-Mean Clustering Hierarchical Clustering DBScan Clustering PCA (Principal Component Analysis) Association Rule Mining Model Deployment >> 30+ Hours Video >> 4 Capstone Projects >> 8+ Case Studies >> 24x7 Support >>ENROLL TODAY & GET DATA SCIENCE INTERVIEW PREPARATION COURSE FOR FREE << | https://www.udemy.com/course/data-science-complete-course/#instructor-1 | UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain. We work for different different MNC including Microsoft,IBM,CISCO,eBay,Amazon, Flipkart etc and a lot of Startups also. We teach you practical Hands on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through Example and Step by Step. We love to help you! | Misc | >=4 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
The Complete Python Course for Machine Learning Engineers | Two Courses Now Included for the Price of One | 4.2 | 503 | 2977 | Created by Mike West | Aug-21 | English | $9.99 | 3h 45m total length | https://www.udemy.com/course/the-complete-python-course-for-machine-learning-engineers/ | Mike West | Creator of LogikBot | 4.3 | 19947 | 236918 | UPDATE: 8/26/2021 - I've added a second course. The second course included with this one is called The Fast Track Introduction to Python for Machine Learning Engineers. I've added a second course to this one that will focus more on the machine learning aspect and less on core Python. There will be some overlap but that's actually good news. The more you see something the easier it will be to remember. I hope the two courses a compliment one another. COURSE REVIEWS This is the best hands-on online class I have ever taken. Very clear instructions. - Donato I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful. -Tony By taking this course my perception has changed and now data science for me is more about data wrangling. Thank you, Mike:) -Archit I have now finished the first one, The complete python course and I have found it extremely structured and clear. I really thank you for your efforts in making these videos. I will now move on to Pandas. I am also looking out for jobs in order to start my career in this exciting field. - Gurukiran A I am really thankful to the instructor for creating such a nice and interactive course, thanks again. - Arun This course does a good job in introducing machine learning in Python. - Vivek Lesson are small, interactive, to the point and knowledge base. -Sanjay Nice course on python programming & intro to libraries. - Sindhura Yes. Accurate match for immediate requirements. Thank you! Looking forward to continuation courses. - Gregory This course was very informative. Taught me about the open source models on which Machine Learning can be practiced. Kudos to the author. Great Job!!! - Mehar Perfect explaining and perfect length, not too long explanations - Henrik I loved the short format of the course. While that is a great thing there are some area which could have been a little longer. Overall, a very good course. - Raymond Really well done !!!!! With this course the programming language itself can be learnt unrelated to any computational task. - Giovanni The hands on examples made learning very easy. I learned a lot about Python and Machine Learning at the same time. I would totally recommend for beginners. - Lumi Simple and easy to understand! - Pavan Clear and easy to follow and understand the topic. - Dennis COURSE OVERVIEW Welcome to The Complete Course for Machine Learning Engineers. This series of courses is the only real world path to attaining a job as a machine learning engineer. Machine learning engineers don't build models every day. If you want to work in the real world then focus on learning Python. That's what this course is... Python!!! This is the first course in a series of courses designed to prepare you for a real-world career as a machine learning engineer. I'll keep this updated and list only the courses that are live. Here is a list of the courses that can be taken right now. Please take them in order. The knowledge builds from course to course. The Complete Python Course for Machine Learning Engineers (This one) Data Wrangling in Pandas for Machine Learning Engineers Data Visualization in Python for Machine Learning Engineers SciKit-Learn in Python for Machine Learning Engineers (NEW) In this course we are going to learn Python using a lab integrated approach. Programming is something you have to do in order to master it. You can't read about Python and expect to learn it. If you take this course from start to finish you'll know the core foundations of Python, you'll understand the very basics of data cleansing and lastly you'll build a traditional machine learning model and a deep learning model. While the course is centered on learning the basics of Python you'll get to see how data cleansing is applied to a data set and how a traditional machine learning model and a deep learning model are built. This course is an applied course on machine learning. Here' are a few items you'll learn: Python basics from A-Z Lab integrated. Please don't just watch. Learning is an interactive event. Go over every lab in detail. Real world Interviews Questions Data Wrangling overview. What is it? Pay attention to the basics, it's what you'll be doing most of your time. Build a basic model build in SciKit-Learn. We call these traditional models to distinguish them from deep learning models. Build a basic Keras model. Keras is becoming the go to Python library for building deep learning models. If you're new to programming or machine learning you might ask, why would I want to learn Python? Python has become the gold standard for building machine learning models in the applied space. The term "applied" simply means the real world. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The key part of that definition is “without being explicitly programmed.” If you're interested in working as a machine learning engineer, data engineer or data scientist then you'll have to know Python. The good news is that Python is a high level language. That means it was designed with ease of learning in mind. It's very user friendly and has a lot of applications outside of the ones we are interested in. In The Complete Course for Machine Learning Engineers we are going to start with the basics. You'll learn how to install Python all the way through building a simple deep learning model using the skills you've learned. As you learn Python you'll be completing labs that will build on what you've learned in the previous lesson so please don't skip any. *Five Reasons to take this Course.* 1) You Want to be a Machine Learning Engineer It's one of the most sought after careers in the world. The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. Without a solid understanding of Python you'll have a hard time of securing a position as a machine learning engineer. 2) The Google Certified Data Engineer Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone. Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google. You can't become a data engineer without learning Python. 3) The Growth of Data is Insane Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month. Almost all real world machine learning is supervised. That means you point your machine learning models at clean tabular data. Python has libraries that are specific to data cleansing. 4) Machine Learning in Plain English Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer. Google expects data engineers and their machine learning engineers to be able to build machine learning models. In this course, you'll learn enough Python to be able to build a deep learning model. 5) You want to be ahead of the Curve The data engineer and machine learning engineer roles are fairly new. While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package. Thanks for interest in The Complete Python Course for Machine Learning Engineers See you in the course!! | https://www.udemy.com/course/the-complete-python-course-for-machine-learning-engineers/#instructor-1 | I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help. I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space. Experience, education and passion I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car. Certifications Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT). Personal Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||||
Deep Learning for Computer Vision with TensorFlow 2 | ConvNets, ResNet, Inception, Faster R-CNN, SSD, TensorFlow Object Detection, YOLOv4, License Plate OCR | 4.1 | 496 | 3258 | Created by CARLOS QUIROS | Jun-22 | English | $13.99 | 11h 58m total length | https://www.udemy.com/course/deep-learning-for-computer-vision-with-tensor-flow-and-keras/ | CARLOS QUIROS | Industrial Engineer and Data Scientist | 4 | 1265 | 10286 | This course is focused in the application of Deep Learning for image classification and object detection. This course originally was designed in TensorFlow version 1.X but now the lessons and codes were updated with TensorFlow version 2.X, mainly by the use of Google Colaboratory(Colab). There is a new course version with extension 2022 with more updated content. If you dont have an available GPU in your local system or you want to experiment in an environment without any previous installation or setup, dont worry you can follow the course smootly because all codes were optimized in Google Colab. The course starts with a concise review of the main concepts in Deep Learning, because this course focused in the application of Deep Learning in the computer vision field. The main computer vision tasks covered in this course are image classification and object detection. After reviewing the deep learning theory you will enter in the study of Convolutional Neural Networks (ConvNets) for image classification studying the following concepts and algorithms: - Image Fundamentals - Loading images in TensorFlow - The building blocks of ConvNets such as: Convolution Operation, Filters, Batch Normalization, ReLU Function, DropOut, Pooling Layers, Dilation, Shared Weights, Image Augmentation, etc - Different ConvNets architectures such as: LeNet5, AlexNet, VGG-16, ResNet Inception. - Many practical applications using famous datasets such as: Covid19 on X-Ray images, CIFAR10, BCCD, COCO dataset, Open Images Dataset V6 through Voxel FiftyOne, ROBOFLOW, You will also learn how to work and collect image data through web scraping with Python and Selenium. Finally in the Object Detection chapter we will explore the theory and the application using Transfer Learning approach using the lastest state of the art algorithms with practical applications. Some of the content in this Chapter is the following: - Theoretical background for Selective Search algorith, - Theoretical background for R-CNN, Fast R-CNN and Faster R-CNN, - Faster R-CNN application on BCCD dataset for detecting blood cells, - Theoretical background for Single Shot Detector (SSD), - Training your customs datasets using different models with TensorFlow Object Detection API - Object Detection on images, videos and livestreaming, - YOLOv2 theory and practical application in a custom dataset (R2D2 dataset) - YOLOv3 practical application in a custom dataset (R2D2 and C3PO dataset) - YOLOv4 theory and practical application in a custom dataset (R2D2 and C3PO dataset) - Practical application for License Plate recognition converting the plates images in raw text format (OCR) with Yolov4, OpenCV and ConvNets Finally you will learn how to construct and train your own dataset through GPU computing running Yolo v2, Yolo v3 and the latest Yolo v4 using Google Colab. You will find in this course a consice review of the theory with intuitive concepts of the algorithms, and you will be able to put in practice your knowledge with many practical examples using your own datasets. This course is very well qualified by the students, some of the inspiring comments are: * Maximiliano D'Amico (5 stars): Very interesting and updated course on YOLO! * Stefan Lankester (5 stars): Thanks Carlos for this valuable training. Good explanation with broad treatment of the subject object recognition in images and video. Showing interesting examples and references to the needed resources. Good explanation about which versions of different python packages should be used for successful results. * Shihab (5 stars): It was a really amazing course. Must recommend for everyone. * Estanislau de Sena Filho (5 stars): Excellent course. Excellent explanation. It's the best machine learning course for computer vision. I recommend it * Areej AI Medinah (5 stars): The course is really good for computer vision. It consists of all material required to put computer vision projects in practice. After building a great understanding through theory, it also gives hands-on experience. * Dave Roberto (5 stars): The course is completely worth it. The teacher clearly conveys the concepts and it is clear that he understands them very well (there is not the same feeling with other courses). The schemes he uses are not the usual ones you can see in other courses, but they really help much better to illustrate and understand. I would give eight stars to the course, but the maximum is five. It's one of the few Udemy courses that has left me really satisfied. The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: [email protected] or by Twitter: @AILearningCQ | https://www.udemy.com/course/deep-learning-for-computer-vision-with-tensor-flow-and-keras/#instructor-1 | Industrial Engineer with more than 20 years in developing and managing business, with vast experience on process analysis and developing business information systems for data science. He has an Industrial Engineering degree from Pontificia Universidad Catolica del Peru (Lima-Peru) and Master in Business Administration (MBA) from ESAN Graduated School of Business (Lima-Peru). He is also an experience developer of machine learning and data science models in many fields of the industry and services like Marketing, Logistics, Finance, Manufacture, Quality Control, Computer Vision, NLP, Deep Learning apps and many others. He wants to share his experience teaching you on a simple and practical way, illustrating concepts based on graphics for better understanding. | Computer Vision | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Artificial Neural Networks for Business Managers in R Studio | You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work | 4.7 | 491 | 99727 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 7h 41m total length | https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Artificial Neural Network (ANN) course that teaches you everything you need to create a Neural Network model in R, right? You've found the right Neural Networks course! After completing this course you will be able to: Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course. If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in R Studio without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create a predictive model using Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 250,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 - Setting up R studio and R Crash course This part gets you started with R. This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Part 2 - Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 - Creating Regression and Classification ANN model in R In this part you will learn how to create ANN models in R Studio. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 - Data Preprocessing In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. Part 5 - Classic ML technique - Linear Regression This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a Neural Network model in R will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use R for Deep Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/neural-network-understanding-and-building-an-ann-in-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Neural Networks | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Text Mining, Scraping and Sentiment Analysis with R | Learn how to use Twitter social media data for your R text mining work. | 3.9 | 489 | 4047 | Created by R-Tutorials Training | Nov-17 | English | $11.99 | 3h 8m total length | https://www.udemy.com/course/r-social-media-mining-scraping-with-twitter/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | Are you an advanced R user, looking to expand your R toolbox? Are you interested in social media sentiment analysis? Do you want to learn how you can get and use Twitter data for your R analysis? Do you want to learn how you can systematically find related words (keywords) to a search term using Twitter and R? Are you interested in creating visualizations like wordclouds out of text data? Do you want to learn which R packages you can use for web scraping and text analysis purposes? If YES came to your mind to some of those points – this course might be tailored towards your needs! This course will teach you anything you need to know about how to handle social media data in R. We will use Twitter data as our example dataset. During this course we will take a walk through the whole text analysis process of Twitter data. At first you will learn which packages are available for social media analysis. You will learn how to scrape social media (Twitter) data and get it into your R session. After that we will filter, clean and structure our text corpus. The next step is the visualization of the text data via wordclouds and dendrograms. And in the last section we will do a whole sentiment analysis by using a common word lexicon. All of those steps are accompanied by exercise sessions so that you can check if you can put the information to work. According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own. Disclaimer required by Twitter: 'TWITTER, TWEET, RETWEET and the Twitter logo are trademarks of Twitter, Inc or its affiliates.' | https://www.udemy.com/course/r-social-media-mining-scraping-with-twitter/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Misc | >=3 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Machine Learning A-Z: Become Kaggle Master | Master Machine Learning Algorithms Using Python From Beginner to Super Advance Level including Mathematical Insights. | 4.5 | 486 | 2985 | Created by Geekshub Pvt Ltd | Mar-19 | English | $14.99 | 36h 19m total length | https://www.udemy.com/course/machine-learning-become-kaggle-master/ | Geekshub Pvt Ltd | BigData and Analytics | 4.3 | 655 | 4021 | Want to become a good Data Scientist? Then this is a right course for you. This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advance level. We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites. We have covered following topics in detail in this course: 1. Python Fundamentals 2. Numpy 3. Pandas 4. Some Fun with Maths 5. Inferential Statistics 6. Hypothesis Testing 7. Data Visualisation 8. EDA 9. Simple Linear Regression 10. Multiple Linear regression 11. Hotstar/ Netflix: Case Study 12. Gradient Descent 13. KNN 14. Model Performance Metrics 15. Model Selection 16. Naive Bayes 17. Logistic Regression 18. SVM 19. Decision Tree 20. Ensembles - Bagging / Boosting 21. Unsupervised Learning 22. Dimension Reduction 23. Advance ML Algorithms 24. Deep Learning | https://www.udemy.com/course/machine-learning-become-kaggle-master/#instructor-1 | Geekshub is an online education company in the field bigdata and analytics. Our aim as a team is to provide best skill-set to our customers so that they can crack any challenge . Many hot cakes of market which are rare to teach have been taught here. We have best trainers training worldwide . They are from IIT, MIT and Stanford. They teach in their own unique fashion, not just by slides but with practical examples and experiences. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Apache Beam | Google Data Flow (Python) | Complete Hands on Apache Beam | Batch & Streaming pipelines | Beam SQL & Google Cloud Dataflow. | 4.5 | 486 | 3795 | Created by Navdeep Kaur | Mar-21 | English | $9.99 | 3h 13m total length | https://www.udemy.com/course/apache-beam/ | Navdeep Kaur | Premium Instructor | TechnoAvengers.com (Founder) | 4.3 | 4118 | 51147 | Apache Beam is future of Big Data technology and is used to build big data pipelines. This course is designed for beginners who want to learn how to use Apache Beam using python language . It also covers google cloud dataflow which is hottest way to build big data pipelines nowadays using Google cloud. This course consist of various hands on to get you comfortable with various topics in Apache Beam.This course will introduce various topics: Architecture Transformations Side Inputs/Outputs Streaming with Google PubSub Windows in Streaming Handling Late elements Using Triggers Google Cloud Dataflow Beam SQL / Beam SQL on GCP By the end of this course, you will find yourself ready to start using Apache Beam in real work environment. What make this course unique - it's concise that's in only 3 hours you will be able to complete it, covers all relevant topics and slides and presentations are really very exciting and easy to understand. Why Apache beam is future of Big Data? 1. It runs on top of popular big data engine like spark, flink, Google data flow. 2. It is used by big giant like Google. 3. It solves the industry biggest problem of migration and unification from one processing engine to another. So if you want to learn future technology , then you are right place. | https://www.udemy.com/course/apache-beam/#instructor-1 | Navdeep is one of the renowned Premium Instructor at Udemy. Navdeep has 12 years of industry experience in different technologies and domains. With 9+ courses and 40,000+ students and rating of 4.5*, she is one of the leading instructors in the field of Big Data & Cloud. Happy Learning! | Python | Founder/Entrepreneur | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Artificial Intelligence II - Hands-On Neural Networks (Java) | Hopfield networks, neural networks, gradient descent and backpropagation algorithms explained step by step | 4.6 | 479 | 5047 | Created by Holczer Balazs | Sep-20 | English | $9.99 | 4h 54m total length | https://www.udemy.com/course/neural-networks-from-scratch-in-java/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21th century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection. Section 1: what are neural networks modeling the human brain the big picture Section 2: Hopfield neural networks how to construct an autoassociative memory with neural networks Section 3: what is back-propagation feedforward neural networks optimizing the cost function error calculation backpropagation and gradient descent Section 4: the single perceptron model solving linear classification problems logical operators (AND and XOR operation) Section 5: applications of neural networks clustering classification (Iris-dataset) optical character recognition (OCR) smile-detector application from scratch In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them. If you are keen on learning methods, let's get started! | https://www.udemy.com/course/neural-networks-from-scratch-in-java/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Artificial Intelligence | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
ZERO to GOD Python 3.8 FULL STACK MASTERCLASS 45 AI projects | Updated for 2020! HTML To Artificial Intelligence Deep Learning Bootcamp Cornell University course w/Machine Learning! | 3.9 | 476 | 18066 | Created by Gopal Shangari | Jun-19 | English | $11.99 | 29h 57m total length | https://www.udemy.com/course/zero-to-pro-python-3-fullstack-data-bootcamp-45-ai-projects/ | Gopal Shangari | Senior Software Engineer | 3.7 | 754 | 45245 | My name is GP. I used AI to classify brain tumors. I have 11 publications on Pubmed. I went to Cornell and taught at UCSF, NIH, Cornell University and Amherst College. We are offering LIVE HELP M-F 9-5 and also outside those hours when online. This course will be continually updated and we answer all questions. We will continue updating content based on both user demand and changes in machine learning and AI. If you have taken a previous bootcamp but still are struggling, this course will fill in the holes and have you applying Python on lots of different projects. You will learn faster by This is the only fullstack course that teaches you everything from basic frontend HTML to Python 3, Machine learning, Tensor Flow, and Artificial Intelligence / Recurrent Neural Networks! This is a large course, but it is still easy! The secret to this course is that to learn rapidly, we present information in small steps, so that no one step seems difficult. Of course, there are lots of steps, so the knowledge builds fast, but its on a very strong foundation. This is the definitely the most advanced yet simple Python fullstack course online. There is no other course ANYWHERE that goes as far into Data Science and Machine learning/ Artificial Intelligence as a stand alone topic, let alone with a FULLSTACK Python course preceding the data science. We can literally take someone with no programming experience and have them doing AI programs in about 2 weeks (or faster if they study daily). Whether you have never programmed before, already know basic syntax, or want to finally advance your skillset, this course is for you! In this course we will teach you HTML, CSS, Bootstrap, Javascript, jQuery and Python 3. With over 170 lectures and more than 30 hours of video this course is extremely comprehensive We cover a wide variety of topics, including: HTML CSS Bootstrap (to make responsive websites fast!) Javascript (to interact with users) jQuery (to further interact with users using clicks and mouseovers) Installing Python Running Python Code Strings External Modules Object Oriented Programming Inheritance Polymorphism Lists Dictionaries Tuples Sets Number Data Types Print Formatting Functions Scope args/kwargs Built-in Functions Debugging and Error Handling Modules File I/O Advanced Methods Decorators/ Advanced Decorators and much more! For Data Science / Machine Learning / Artificial Intelligence 1. Machine Learning 2. Training Algorithm 3. SciKit 4. Data Preprocessing 5. Dimesionality Reduction 6. Hyperparemeter Optimization 7. Ensemble Learning 8. Sentiment Analysis 9. Regression Analysis 10.Cluster Analysis 11. Artificial Neural Networks 12. TensorFlow 13. TensorFlow Workshop 14. Convolutional Neural Networks 15. Recurrent Neural Networks Traditional statistics and Machine Learning 1. Descriptive Statistics 2.Classical Inference Proportions 3. Classical InferenceMeans 4. Bayesian Analysis 5. Bayesian Inference Proportions 6. Bayesian Inference Means 7. Correlations 11. KNN 12. Decision Tree 13. Random Forests 14. OLS 15. Evaluating Linear Model 16. Ridge Regression 17. LASSO Regression 18. Interpolation 19. Perceptron Basic 20. Training Neural Network 21. Regression Neural Network 22. Clustering 23. Evaluating Cluster Model 24. kMeans 25. Hierarchal 26. Spectral 27. PCA 28. SVD 29. Low Dimensional You will get lifetime access to over 180 lectures plus corresponding Notebooks for the lectures! This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back. Learn Python and AI in the easiest possible way, so you can advance your career quickly and easily. Who is the target audience? Beginners who have never programmed before. People who took a programming bootcamp but are looking to apply that knowledge to build something other than very basic projects. Intermediate Python programmers who want to understand Artificial Intelligence Programming. | https://www.udemy.com/course/zero-to-pro-python-3-fullstack-data-bootcamp-45-ai-projects/#instructor-1 | BA Degree from Computer Science and Neuroscience from Cornell University in Ithaca NY MD from Medical College of Ohio (now University of Toledo College of Medicine) Statistics Certificate from University of California San Francisco Worked at UCSF as Data Science Programmer from 2008-2012 National Institute of Health (NIH) as Data Science Programmer 2013-2018 NIH as Machine Learning Instructor 2016-2018 Nextcore AI data contracting for NIH 2016-present | Python | Senior Role | >=3 | Below 1K | >=15K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Species Distribution Models with GIS & Machine Learning in R | Mapping Habitat Suitability for Conservation Using Machine Learning and GIS in R | Bestseller | 4.5 | 467 | 2723 | Created by Minerva Singh | Oct-22 | English | $11.99 | 3h 47m total length | https://www.udemy.com/course/species-distribution-models-with-gis-machine-learning-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Are you an ecologist/conservationist looking to carry out habitat suitability mapping?Are you an ecologist/conservationist looking to get started with R for accessing ecological data and GIS analysis?Do you want to implement practical machine learning models in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals. In this course, actual spatial data from Peninsular Malaysia will be used to give a practical hands-on experience of working with real life spatial data for mapping habitat suitability in conjunction with classical SDM models like MaxEnt and machine learning alternatives such as Random Forests. The underlying motivation for the course is to ensure you can put spatial data and machine learning analysis into practice today. Start ecological data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your GIS and Machine Learning skills in R. So Many R based Machine Learning and GIS Courses Out There, Why This One? This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real ecological data in R. Plus, you will gain exposure to working your way through a common ecological modelling technique- species distribution modelling (SDM) using real life data. Students will also gain exposure to implementing some of the most common Geographic Information Systems (GIS) and spatial data analysis techniques in R. Additionally, students will learn how to access ecological data via R. You will learn to harness the power of both GIS and Machine Learning in R for ecological modelling. I have designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Yes, even non-ecologists can get started with practical machine learning techniques in R while working their way through real data. What you will Learn in this Course This is how the course is structured: Introduction – Introduction to SDMs and mapping habitat suitabilityThe Basics of GIS for Species Distribution Models (SDMs) – You will learn some of the most common GIS and data analysis tasks related to SDMs including accessing species presence data via RPre-Processing Raster and Spatial Data for SDMs - Your R based GIS training and will continue and you will earn to perform some of the most common GIS techniques on raster and other spatial dataClassical SDM Techniques - Introduction to the classical models and their implementation in R (MaxENT and Bioclim)Machine Learning Models for Habitat Suitability - Implement and interpret common ML techniques to build habitat suitability maps for the birds of Peninsular Malaysia. It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts . However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course. | https://www.udemy.com/course/species-distribution-models-with-gis-machine-learning-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
Machine Learning - Regression and Classification (math Inc.) | A complete Beginner to Advance level guide to Machine Learning. Hands-on Learning approach with in-depth math concepts | 4.2 | 462 | 59849 | Created by Sachin Kafle | Apr-21 | English | $9.99 | 17h 0m total length | https://www.udemy.com/course/machine-learning-regression-and-classification-math-inc/ | Sachin Kafle | Founder of CSAMIN & Bit4Stack Tech Inc. [[Author, Teacher]] | 4.1 | 3043 | 181551 | Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error. Topics covered in this course: 1. Lecture on Information Gain and GINI impurity [decision trees] 2. Numerical problem related to Decision Tree will be solved in tutorial sessions 3. Implementing Decision Tree Classifier in workshop session [coding] 4. Regression Trees 5. Implement Decision Tree Regressor 6. Simple Linear Regression 7. Tutorial on cost function and numerical implementing Ordinary Least Squares Algorithm 8. Multiple Linear Regression 9. Polynomial Linear Regression 10. Implement Simple, Multiple, Polynomial Linear Regression [[coding session]] 11. Write code of Multivariate Linear Regression from Scratch 12. Learn about gradient Descent algorithm 13. Lecture on Logistic Regression [[decision boundary, cost function, gradient descent.....]] 14. Implement Logistic Regression [[coding session]] | https://www.udemy.com/course/machine-learning-regression-and-classification-math-inc/#instructor-1 | Sachin Kafle is a Python and Java developer, ethical hacker and social activist. His interest's lies in software development and integration practices in the areas of computation, quantitative fields of trade. His technological interests include Python, C, Java, C# programming. He has been involved in teaching since 2013. Sachin is a engineer of Computer Science (B.E. Computer Science). He is also an instructor on his previously made some geek Youtube channel. He has been giving free classes mostly for students who have not been able to pay for expensive classes in his country. | Machine Learning | Founder/Entrepreneur | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=1.5 Lakh | |||||||||||||||||
The Data Science MicroDegree: Data Analysis & Visualization | We start from absolute Python scratch and gradually progress into NumPy, Pandas, Matplotlib & Seaborn for data analysis | 4.1 | 448 | 30222 | Created by Abhishek Pughazh | May-21 | English | $9.99 | 4h 26m total length | https://www.udemy.com/course/datasciencemicrodegree/ | Abhishek Pughazh | I'm a Python Freelancer, creating cool stuff. | 4.5 | 1607 | 85858 | There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial, we build on what had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This comprehensive course will be your guide to learning how to use the power of Python to analyze data and create beautiful visualizations. This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! "Data Scientist" has been ranked the Number #1 Job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will still be successful in this course! I can't wait to see you in class. In This Course You'll Learn: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Use matplotlib and seaborn for data visualizations | https://www.udemy.com/course/datasciencemicrodegree/#instructor-1 | I'm a Business Analyst and a programming enthusiast from Chennai, India. Having spent a huge chunk of my teenage procrastinating over how hard programming was, I realized that it actually wasn't. The market is crowded with instructors who tend to over-exaggerate its level of difficulty. With all the experience that I had while learning to program on my own, I've managed to figure out a solid way to learn to code. And I wanted to help the community with the same. | Misc | >=4 | Below 1K | >=30K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Support Vector Machines in Python: SVM Concepts & Code | Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning | 4.4 | 441 | 84820 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 6h 15m total length | https://www.udemy.com/course/machine-learning-adv-support-vector-machines-svm-python/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right? You've found the right Support Vector Machines techniques course! How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through Decision tree. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy | https://www.udemy.com/course/machine-learning-adv-support-vector-machines-svm-python/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Python | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Complete Machine Learning & Data Science with Python| ML A-Z | Learn Numpy, Pandas, Matplotlib, Seaborn, Scipy, Supervised & Unsupervised Machine Learning A-Z and feature engineering | 4.7 | 439 | 25161 | Created by Goeduhub Technologies | Jun-21 | English | $9.99 | 11h 13m total length | https://www.udemy.com/course/complete-machine-learning-data-science-libraries-with-python/ | Goeduhub Technologies | Technical Training Provider Company. | 4.1 | 1814 | 53735 | Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights. This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included. 1) Project - Customer Segmentation Using K Means Clustering 2) Project - Fake News Detection using Machine Learning (Python) 3) Project COVID-19: Coronavirus Infection Probability using Machine Learning 4) Project - Image compression using K-means clustering | Color Quantization using K-Means This course include topics --- What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement Learning Python for Data Analysis- Numpy Working envirnment- Google Colab Anaconda Installation Jupyter Notebook Data analysis-Pandas Matplotlib What is Supervised Machine Learning Regression Classification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision Tree K-Nearest Neighbor(KNN) Algorithm Support Vector Machine Algorithm Random Forest Algorithm I What is UnSupervised Machine Learning Types of Unsupervised Learning Advantages and Disadvantages of Unsupervised Learning What is clustering? K-means Clustering Image compression using K-means clustering | Color Quantization using K-Means Underfitting, Over-fitting and best fitting in Machine Learning How to avoid Overfitting in Machine Learning Feature Engineering Teachable Machine Python Basics In the recent years, self-driving vehicles, digital assistants, robotic factory staff, and smart cities have proven that intelligent machines are possible. AI has transformed most industry sectors like retail, manufacturing, finance, healthcare, and media and continues to invade new territories. Everyday a new app, product or service unveils that it is using machine learning to get smarter and better. NOTE :- In description reference notes also provided , open reference notes , there is Download link. You can download datasets there. | https://www.udemy.com/course/complete-machine-learning-data-science-libraries-with-python/#instructor-1 | Find List Of Best Online Courses By World's Best Instructors Lists are Available at Our Website(GOEDUHUB). Best Courses in Data Science, Artificial Intelligence, Machine Learning, Python, Cloud Computing and Many More... We provide comprehensive training in Industrial Automation, Artificial Intelligence(AI), Machine Learning(ML) & Deep Learning, Python Programming and Data Science. We are providing a broad foundation for a revolution in higher education worldwide. The advent of the Internet and other information technologies can make teaching and research readily available to scholars and students across the globe. With the changing global scenario and India turning out to be knowledge based economy like US, there is a huge requirement of technology professionals worldwide. The Need of interactive learning and maintaining high quality standards in technology education is the need of the hour. With over 10 million upcoming new jobs in emerging technology sectors like Artificial Intelligence(AI), Machine Learning(ML), Deep Learning(DL), Python Programming, Cloud Computing, Embedded systems and Robotics, young India must opt for technology training that comes from the premier education schools-training that is high quality, reliable, cutting edge and complete. Such training will not only equip students to participate in the job-rich emerging sectors, it will also allow existing professionals to re-skill themselves with more up-to-date technology knowledge. | Machine Learning | >=4 | Below 1K | >=25K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Apache Spark 2.0 + Java : DO Big Data Analytics & ML | Project Based, Hands-on Practices, Spark SQL, Spark Streaming, Java Setup and building real world applications | 4.5 | 428 | 3815 | Created by V2 Maestros, LLC | Jan-17 | English | $9.99 | 7h 44m total length | https://www.udemy.com/course/apache-spark-20-java-do-big-data-analytics-ml/ | V2 Maestros, LLC | Big Data / Data Science Experts | 50K+ students | 4.2 | 4162 | 76176 | Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This course teaches you Apache Spark 2.0 with Java, trains you in building Spark Analytics and machine learning programs and helps you practice hands-on (2K LOC code samples !) with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable. Java is the main technology used today to build industry-grade applications and coming that with Spark gives you unlimited ability to build cutting edge applications. Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages. The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you Learn solid fundamentals of the domainSee demos, train and execute solid examplesPractice hands-on and validate it with solutions providedApply knowledge you acquired in an end-to-end real life project Taught by an expert in the field, you will also get prompt response to your queries and excellent support from Udemy. | https://www.udemy.com/course/apache-spark-20-java-do-big-data-analytics-ml/#instructor-1 | V2 Maestros is dedicated to teaching big data / data science courses to students all over the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building Big Data and Data Science talent across the world. | Data Analyst | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Learn Machine Learning in 21 Days | Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included. | 3.9 | 428 | 79188 | Created by Code Warriors, Mayank Bajaj, Gaurav Sharma, Anup Mor | Apr-21 | English | $9.99 | 4h 36m total length | https://www.udemy.com/course/learn-machine-learning-in-21-days/ | Code Warriors | The best place to learn, code and conquer - Once you have it | 4 | 4869 | 305419 | Interested in the field of Machine Learning? Then this course is for you! This course has been designed by Code Warriors the ML Enthusiasts so that we can share our knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time, we dive deep into Machine Learning. It is structured the following way: You can do a lot in 21 Days. Actually, it’s the perfect number of days required to adopt a new habit! What you'll learn:- 1.Machine Learning Overview 2.Regression Algorithms on the real-time dataset 3.Regression Miniproject 4.Classification Algorithms on the real-time dataset 5.Classification Miniproject 6.Model Fine-Tuning 7.Deployment of the ML model | https://www.udemy.com/course/learn-machine-learning-in-21-days/#instructor-1 | Hi, We are Code Warriors an E learning organisation . This is our Udemy Handle where we will provide you some awesome courses with very basic price. The courses will be very much informative and you will enjoy a lot. We focus on your learning in an enjoying manner so you don't get bored. | Machine Learning | >=3 | Below 1K | >=50K | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Data Lake, Firehose, Glue, Athena, S3 and AWS SDK for .NET | Leverage AWS Kinesis Data Firehose, AWS Glue, S3, Athena and the AWS SDK to build a Data Lake. | 4.2 | 427 | 1926 | Created by Darren Cox | Mar-20 | English | $9.99 | 1h 54m total length | https://www.udemy.com/course/data-lake-firehose-glue-athena-s3-and-aws-sdk-for-net/ | Darren Cox | AWS Certified Solution Architect/Developer & Music lover | 4.3 | 478 | 2216 | The purpose of this class is to demonstrate a proof of concept using a series of lab exercise's (in the AWS Console using AWS Kinesis Data Firehose, AWS Glue, S3, Athena and the AWS SDK, with C# code using the AWS SDK) of building a Data Lake in the AWS ecosystem. In this class, we will be sending data from a local SQL Server database to AWS RDS securely, and automatically. I am utilizing the .NET SDK for AWS, however, this could easily be migrated to your language of choice once you understand the concepts that I am teaching. I will be providing the full working source code of this proof of concept. I will also provide a working example of creating Parquet files and sending to S3 (without using Firehose). | https://www.udemy.com/course/data-lake-firehose-glue-athena-s3-and-aws-sdk-for-net/#instructor-1 | I have been a .NET software developer for the past 20 years, creating many different applications, which include service applications, web applications, and client server applications. I have achieved two different AWS certifications: AWS Certified Solution Architect and AWS Certified Developer. I also love music, and I play the 5-string banjo (Scruggs style). | AWS | Architect | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Power BI: Advanced Data Transformations and Modeling | Learn how to clean, optimize and model data tables to develop effective, insightful and actionable Power BI reports | Bestseller | 4.3 | 427 | 10590 | Created by Enterprise DNA, Sam McKay | Sep-21 | English | $9.99 | 3h 52m total length | https://www.udemy.com/course/power-bi-advanced-data-transformations-and-modeling/ | Enterprise DNA | Empowering Power BI Users to Change Their World | 4.5 | 3316 | 48657 | An outline of this training course This course covers in detail the query editor and how to develop effective Power BI data models. When starting out with Power BI you can sometimes overlook how important these two areas inside of Power BI are to developing an effective reporting solution. This course makes sure that you can see the immense value of doing work in these areas right can be. Covered are intermediate to advanced techniques that will enable you to optimize your raw data tables and then connect them into a functioning analytical model that you can ultimately overlay DAX formula to and get the correct results, that will answer your analytical questions. Details of what you will learn during this course Learn - best practice techniques when using the query editor Learn - how to work with 'M' code and the advanced editor Understand - the row and column query transformation options Implement - advanced data cleaning and transformation techniques Work - through end to end examples of querying multiple tables Learn - how to think about and manage the data model Learn - effective techniques applicable to any data scenario Apply - advanced data modeling techniques to your own models Understand - advanced modeling scenarios and situations Learn - how to effectively organize your models What you get with the course 4 hours of course videos Demo dataset and model to practice advanced querying and data modeling with Testimonials "From my first days working with Power BI. Sam and Enterprise DNA has been my go-to people for learning about how to do things in Power BI. From modeling to DAX, visualizations to formatting, and publishing reports to creating dashboards and apps." - Ian Besmond "I have trawled the internet and tried at least 3 Power BI courses. This is the first one that includes a methodology for Data Modeling and analytics that is intuitive and makes sense. I went from very basic knowledge of Power BI to comfortable with the concept of DAX and setting up attractive reports in 1 day. A course cannot get better than that!" - Karl Griffin "Sam - you have built an incredible community of people who have become passionate about Power BI. Your videos (both publicly available and commercially available by subscription) are an invaluable tool to BI users at all levels. I learned a ton about proper data modeling for analytics, DAX complex queries, using M-Query, organizing information properly, and much more. I highly recommend the membership - it has proven to be a prudent investment." - Tiran | https://www.udemy.com/course/power-bi-advanced-data-transformations-and-modeling/#instructor-1 | Enterprise DNA empowers analysts and teams to raise their standard of reporting and analysis from basic to high value through our customized learning pathways, skills assessment tools, comprehensive resources, well-structured online training, and support community. For over five years, we have trained our students to unlock the potential of their data and demystify Power BI as a business intelligence tool; turning over 100,000 data professionals into Power BI authorities. Power BI is an amazingly powerful analytical tool. Harnessing its potential can create so many possibilities and Enterprise DNA can guide you in the journey. | Power BI | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
2 Real World Azure Data Engineer Project End to End | Engineering Project Building from scratch including designing, architecting, implementing solution and overall testing. | 4.1 | 424 | 3250 | Created by Deepak Goyal | Sep-22 | English | $54.99 | 7h 11m total length | https://www.udemy.com/course/two-real-world-azure-data-engineer-projects-end-to-end/ | Deepak Goyal | Microsoft Certified Trainer | Architect| Top Voice LinkedIn | 4.3 | 520 | 4444 | This course will help you in preparing and mastering your Azure Data engineering Concepts. It is not like any random project like covid, or twitter analysis. These project is real world projects on which I personally worked and developed it for big clients. Highlights of the Course: Designed to keep only précised information no beating around the bush. (To save your time). Real time implementation, no dummy use case. Can be added as part of your resume. It will help you to showcase your experience in interviews and discussion. Involve complex architecture solution which is aligned with industry best practices. Single projects involve various component integration like ADF, ADLS, Databricks, Azure SQL DB, Key Vault. Solves the problem of real time experience for new Data engineers. This course has been developed in mind to keep all the best practices followed in the Industry as an data engineering project and solution. Technologies involved: Azure Data Lake Storage Gen 2 Azure Data Factory Data Factory Pipeline Azure Functions Azure Key Vault Azure SQL DB SSMS AWS S3 Bucket Connect ADF to Databricks Connect Databricks to SQL Server Connect Databricks to ADLS Connect S3 to Azure Cloud Triggers SAS token Create Secrets scope in Databricks Store secretes in Key Vault and access them What you will learn after this course: How to think, design and develop the solution in the data engineering world. How to create the architecture diagram for data engineering projects. How to Create Azure Data Factory Account How to Create Azure Data Lake Storage Gen 2 account. How to Create Azure Databricks Workspace. How to create S3 storage account. How to create Azure Function. How to implement logic in the Databricks notebook using pyspark. How to connect ADF to Databricks. How to chain the multiple pieces together in project. How to create Azure SQL Server. How to load the data from file to Azure SQL server. How to connect Databricks notebook with Azure SQL Server. How to Store secrets in the Azure Key Vault. | https://www.udemy.com/course/two-real-world-azure-data-engineer-projects-end-to-end/#instructor-1 | Hi, I'm Deepak Goyal, a certified Big data, and Azure Cloud Solution Architect. I have 13+ Years of experience in the IT industry and 10+ Years of experience in Big data world. I was among the few who has worked on Hadoop Big data analytics (since the Year 2011) before the popular advancement and adoption of Public cloud providers like AWS, Microsoft Azure, or Google Cloud Platform (GCP) I help businesses to define the data-driven architecture and make robust data platform over the cloud to scale up their business. Writing about Microsoft Azure technologies is one of my favorite works outside the office. I help people to understand cloud concepts and technologies like Azure Data Factory, Azure DataBricks, Apache Spark, Azure Synapse Analytics, Azure Key Vault, Encryption Decryption, Azure Blob Storage, Azure monitor, logging, Snowflake cloud data warehouse, and many more complex tools and technologies. I am a famous Azure blogger, my blog ranks number 1 on google search for more than 100+ keywords. | Data Engineer | Architect | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Science with Python Complete Course | Data Science 2021 : Complete Data Science | 4.1 | 422 | 53657 | Created by Prashant Mishra | Jan-22 | English | $9.99 | 20h 13m total length | https://www.udemy.com/course/data-science-with-python-complete-course/ | Prashant Mishra | Teacher | 4.2 | 951 | 79722 | Today Data Science and Machine Learning are used in almost every industry, including automobiles, banks, health, telecommunications, telecommunications, and more. As the manager of Data Science and Machine Learning, you will have to research and look beyond common problems, you may need to do a lot of data processing. test data using advanced tools and build amazing business solutions. However, where and how will you learn these skills required in Data Science and Machine Learning? DATA SCIENCE COURSE-OVERVIEW Getting Started with Data Science Define Data Why Data Science? Who is a Data Scientist? What does a Data Scientist do? The lifecycle of Data Science with the help of a use case Job trends Data Science Components Data Science Job Roles Math Basics Multivariable Calculus Functions of several variables Derivatives and gradients Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function Cost function Plotting of functions Minimum and Maximum values of a function Linear Algebra Vectors Matrices Transpose of a matrix The inverse of a matrix The determinant of a matrix Dot product Eigenvalues Eigenvectors Optimization Methods Cost function/Objective function Likelihood function Error function Gradient Descent Algorithm and its variants (e.g., Stochastic Gradient Descent Algorithm) Programming Basics R Programming for Data Science History of R Why R? R Installation Installation of R Studio Install R Packages. R for business Features of R Basic R syntax R programming fundamentals Foundational R programming concepts such as data types, vectors arithmetic, indexing, and data frames How to perform operations in R including sorting, data wrangling using dplyr, and data visualization with ggplot2 Understand and use the various graphics in R for data visualization. Gain a basic understanding of various statistical concepts. Understand and use hypothesis testing method to drive business decisions. Understand and use linear, non-linear regression models, and classification techniques for data analysis. Working with data in R Master R programming and understand how various statements are executed in R. Python for Data Science Introduction to Python for Data Science Introduction to Python Python Installation Python Environment Setup Python Packages Installation Variables and Datatypes Operators Python Pandas-Intro Python Numpy-Intro Python SciPy-Intro Python Matplotlib-Intro Python Basics Python Data Structures Programming Fundamentals Working with data in Python Object-oriented programming aspects of Python Jupyter notebooks Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave Perform data analysis and manipulation using data structures and tools provided in the Pandas package Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline Use the matplotlib library of Python for data visualization Extract useful data from websites by performing web scraping using Python Integrate Python with MapReduce Data Basics Learn how to manipulate data in various formats, for example, CSV file, pdf file, text file, etc. Learn how to clean data, impute data, scale data, import and export data, and scrape data from the internet. Learn data transformation and dimensionality reduction techniques such as covariance matrix plot, principal component analysis (PCA), and linear discriminant analysis (LDA). Probability and Statistics Basics Important statistical concepts used in data science Difference between population and sample Types of variables Measures of central tendency Measures of variability Coefficient of variance Skewness and Kurtosis Inferential Statistics Regression and ANOVA Exploratory Data Analysis Data visualization Missing value analysis Introduction to Big Data Introduction to Hadoop Introduction to Tableau Introduction to Business Analytics Introduction to Machine Learning Basics Supervised vs Unsupervised Time Series Analysis Text Mining Data Science Capstone Project Science and Mechanical Data require in-depth knowledge on a variety of topics. Scientific data is not limited to knowing specific packages/libraries and learning how to use them. Science and Mechanical Data requires an accurate understanding of the following skills, Understand the complete structure of Science and Mechanical Data Different Types of Data Analytics, Data Design, Scientific Data Transfer Features and Machine Learning Projects Python Programming Skills which is the most popular language in Science and Mechanical Data Machine Learning Mathematics including Linear Algebra, Calculus and how to apply it to Machine Learning Algorithms and Science Data Mathematics and Mathematical Analysis of Data Science Data Science Data Recognition Data processing and deception before installing Learning Machines Machine learning Ridge (L2), Lasso (L1), and Elasticnet Regression / Regularization for Machine Learning Selection and Minimization Feature for Machine Learning Models Selection of Machine Learning Model using Cross Verification and Hyperparameter Tuning Analysis of Machine Learning Materials Groups In-depth learning uses the most popular tools and technologies of today. This Data Science and Machine Learning course is designed to consider all of the above, True Data Science and Machine Learning A-Z Course. In most Data Science and Machine Learning courses, algorithms are taught without teaching Python or this programming language. However, it is very important to understand language structure in order to apply any discipline including Data Science and Mechanical Learning. Also, without understanding Mathematics and Statistics it is impossible to understand how other Data Science and Machine Learning algorithms and techniques work. Science and Mechanical Data is a set of complex linked topics. However, we strongly believe in what Einstein once said, "If you can't explain it easily, you didn't understand it well enough." As a teacher, I constantly strive to reach my goal. This is one comprehensive course in Science and Mechanical Data that teaches you everything you need to learn Science and Mechanical Data using simple examples with great depth. As you will see from the preview talks, some of the more complex topics are explained in simple language. Some important skills you will learn, Python Programming Python is listed as the # 1 language for Data Science and Mechanical Data. It is easy to use and rich with various libraries and functions required to perform various Data Science and Machine Learning activities. In addition, it is the most widely used and automated language for the use of many Deep Learning frameworks including Tensorflow and Keras. Advanced Mathematics Learning Machine Mathematics is the foundation of Data Science in general and Learning Machines in particular. Without understanding the meanings of Vectors, Matrices, their operations and understanding Calculus, it is impossible to understand the basics of Data Science and Machine Learning. The Gradient Declaration of Basic Neural Network and Mechanical Learning is built on the foundations of Calculus and Derivatives. Previous Statistics for Data Science It is not enough to know only what you are saying, in the middle, the mode, etc. Advanced Techniques for Science and Mechanical Data such as feature selection, size reduction using PCA are all based on previous Distribution and Statistical Significance calculations. It also helps us to understand the operation of the data and use the appropriate machine learning process to get the best results from various Data Science and Mechanical Learning techniques. Data recognition As they say, the picture costs a thousand words. Data identification is one of the most important methods of Data Science and Mechanical Data and is used for Analytical Data Analysis. In that, we analyze the data visually to identify patterns and styles. We will learn how to create different sites and charts and how to analyze them for all practical purposes. Feature Selection plays an important role in Machine Learning and Visualization Data is its key. Data processing Scientific Data requires extensive data processing. Data Science and Machine Learning specialists spend more than 2/3 of their time analyzing and analyzing data. Data can be noisy and never in good condition. Data processing is one of the most important ways for Data Science and Mechanics to learn to get the best results. We will be using Pandas which is a well-known Python data processing library and various other libraries for reading, analyzing, processing and cleaning data. Machine learning Heart and Soul Data Science is a guessing skill provided by algorithms from the Deep Learning and Learning Machines. Machine learning takes the complete discipline of Data Science ahead of others. We will integrate everything we have learned in previous sections and build learning models for various machines. The key features of Machine Learning are not only ingenuity but also understanding of the various parameters used by Machine Learning algorithms. We will understand all the key parameters and how their values affect the outcome in order to build the best machine learning models. | https://www.udemy.com/course/data-science-with-python-complete-course/#instructor-1 | I am Computer Science Graduate in 2021 and with a passion for teaching, started back as a BDA in various Ed-tech companies, which increased a little more passion towards this industry to explore. Have trained more than 5000+ Individual students one-on-one and group-based, which not only found my classes very interesting but also developed a huge scope of job opportunities in the future. | Python | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=50K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Tensorflow and Keras For Neural Networks and Deep Learning | Master the Most Important Deep Learning Frameworks (Tensorflow & Keras) for Python Data Science | 4.5 | 414 | 10632 | Created by Minerva Singh | Nov-22 | English | $9.99 | 7h 58m total length | https://www.udemy.com/course/tensorflow-and-keras-for-neural-networks-and-deep-learning/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON! It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- Tensorflow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE: This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python.. This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level. THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE! But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.. This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework. Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow & Keras and give you a one-of-a-kind grounding in these frameworks! DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE: • A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • A comprehensive presentation about Tensorflow & Keras installation and a brief introduction to the other Python data science packages • Brief introduction to the working of Pandas and Numpy • The basics of the Tensorflow syntax and graphing environment • The basics of the Keras syntax • Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow & Keras frameworks • You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow & Keras BUT, WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE: You’ll start by absorbing the most valuable Python Tensorflow and Keras basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life. After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow and Keras. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !! The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities. This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.. After each video you will learn a new concept or technique which you may apply to your own projects! JOIN THE COURSE NOW! | https://www.udemy.com/course/tensorflow-and-keras-for-neural-networks-and-deep-learning/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Deep Learning | Data Scientist | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Python and Data Science for beginners | Learn hands-on data science and Python from scratch | 4 | 413 | 41643 | Created by Bluelime Learning Solutions | Jun-21 | English | $9.99 | 4h 23m total length | https://www.udemy.com/course/introduction-to-data-science-with-python-for-beginners/ | Bluelime Learning Solutions | Learning made simple | 4.1 | 36082 | 742296 | Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development. Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights. Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. This course is a beginners course that will introduce you to some basics of data science using Python. What You Will Learn How to set up environment to explore using Jupyter Notebook How to import Python Libraries into your environment How to work with Tabular data How to explore a Pandas DataFrame How to explore a Pandas Series How to Manipulate a Pandas DataFrame How to clean data How to visualize data | https://www.udemy.com/course/introduction-to-data-science-with-python-for-beginners/#instructor-1 | Bluelime is UK based and creates quality easy to understand eLearning solutions .All our courses are 100% video based. We teach hands –on- examples that teach real life skills . Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need. | Python | >=4 | Below 1K | >=40K | >=4 | Below 1 Lakh | >=5 Lakh | ||||||||||||||||||
NLP and Text mining with python(for absolute beginners only) | Learn Natural Language Processing using Python from experts with hands on examples and practice sessions. | 3.9 | 410 | 9944 | Created by Statinfer Solutions | Oct-18 | English | $9.99 | 2h 21m total length | https://www.udemy.com/course/natural-language-processing-made-easy-using-python/ | Statinfer Solutions | Data Science starts here! | 4.1 | 599 | 25687 | Want to know how NLP algorithms work and how people apply it to solve data science problems? You are looking at right course! This course has been created, designed and assembled by professional Data Scientist who have worked in this field for nearly a decade. We can help you to understand the NLP while keeping you grounded to the implementation on real and data science problems. We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what coming next. Here is how the course is going to work: Session 1: Get introduced to NLP and text mining basics, NLTK package and learn how to prepare unstructured data for further processing. Session 2: Lets you understand sentimental analysis using a case study and a practice session. Session 3: Teaches you document categorization using various machine learning algorithms. Features: Fully packed with LAB Sessions. One to learn from and one to do it by yourself. Course includes Python code, Datasets, ipython notebook and other supporting material at the beginning of each section for you to download and use on your own. | https://www.udemy.com/course/natural-language-processing-made-easy-using-python/#instructor-1 | Statinfer is the data science e-learning solutions provider. We provide online and class room training on leading data science tools and techniques. Our focus is on data analytics, machine learning, and AI. The tools that we work on are R, Python, Tensor Flow and Spark. Statinfer is created by data scientists who understand the dynamics of the current business. Our courses are not merely academic, instead, there are many industrial applications and examples. The creators assembled the course, well studied the topics with a clear understanding and had designed the curriculum. Each course has ample amount of self-practicing labs, quizzes and projects on real data to get an exposure to real world problems. | NLP | >=3 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
SQL and Data Visualization - The Complete Bootcamp | Become an expert in SQL, Data Analysis, Business Intelligence & Data Visualization. Learn SQL from A to Z | 4.5 | 406 | 2050 | Created by Raffi Sarkissian | SQL | PostgreSQL | Metabase | May-20 | English | $9.99 | 5h 24m total length | https://www.udemy.com/course/sql-data-visualization-the-complete-course/ | Raffi Sarkissian | SQL | PostgreSQL | Metabase | Head of Monetization & BI | 4.4 | 465 | 2361 | In this course you will learn how to write and execute SQL queries on your own database. Starting with basic SQL, we will learn how to use PostgreSQL to make advanced data analysis, graphs, reports and tables. Who should attend this SQL and Data Viz course? You who are dependent on Business Analysts to create Reports You who are limited to Excel or Spreadsheet for your data analyses You who would like to do some in-depth analysis and blow your boss or client away You're the one who always said, "I need to learn SQL!" You who find your business analysis dull and graphless. Why learn SQL? Whether you are a Marketer, Product Marketer, Developer, Sales or Manager, SQL is one of the most sought-after skills in the job market. This extraordinary skill takes your CV into another world, the world of autonomy and rigor in your reporting. To learn SQL and Data Viz, you don't need to have a "Business Analyst" career plan. This skill will serve you in all areas, just like some tools such as Excel or Google Spreadsheet have served you well. The only difference? SQL is much more advanced than these! Become an expert in data visualization You'll learn how to become a business analyst and produce reports that are as visual as they are effective. In just a few clicks, create your own PostgreSQL database and connect the best tools to make your analyses dynamic, attractive and orderly. You can then create your own charts, histograms, curves and world maps for specific business situations. Learn SQL from A to Z SQL will have no secrets from you. Of course, we'll go over the basics. Then, you'll even become an expert as you get more and more powerful as you watch the videos. CASE WHEN 'become better in analysis' THEN 'join this course' ELSE 'keep working on Excel' END AS expert_sql_and_data_viz The tools you will master - Metabase, the perfect data visualization tool to run your data analysis - Heroku, to host your database - PGAdmin, to create your database and data set Why is this course different from the others? 1- You will apply SQL in real business cases that you will be able to reuse 2- You will work on your own database 3- You will create your own graphs in a few minutes 4- You will progress step by step in the SQL language 5- I will answer all your questions in a few minutes Anyway, I just wanted to tell you that I'm not from the Data world. I'm a Marketing Manager who got tired of doing his reports in Excel and wanted to take it to the next level by learning SQL! So, if I did it, you can do it. So, join me in this course "SQL & Data Visualization: the complete guide", and don't hesitate to leave a note if you like this course ✌️ Raffi | https://www.udemy.com/course/sql-data-visualization-the-complete-course/#instructor-1 | Hello, I am Raffi Sarkissian, one of the co-founders of Lago, the no-code data tool for Growth Teams. Previously, I was the Head of Growth Operations & Business Intelligence at Qonto, a 500-person fintech that raised €135 million in funding. I manage a team of 4 data analysts. This has allowed me to install or use powerful data visualization tools like Metabase or Tableau and to work on Postgresql databases or Warehouses like Snowflake. My expertise allows me to teach beginners the art of SQL and data analysis in a simple, fun and multi-step way: 1- Installing a database with PGAdmin, a data visualization tool with Metabase and a server with Heroku. 2- The basics of SQL3- Building graphs and BI dashboards for a first step in the world of data visualization4- Advanced SQL to meet business needs (cohorts, median, quartiles, series generation etc...) Kind regards Raffi | SQL | Head/Director | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Applied Time Series Analysis in Python | Use Python and Tensorflow to apply the latest statistical and deep learning techniques for time series analysis | 4.2 | 402 | 1950 | Created by Marco Peixeiro | Jul-22 | English | $9.99 | 6h 56m total length | https://www.udemy.com/course/applied-time-series-analysis-in-python/ | Marco Peixeiro | Data Scientist and Instructor | 4.2 | 402 | 1950 | This is the only course that combines the latest statistical and deep learning techniques for time series analysis. First, the course covers the basic concepts of time series: stationarity and augmented Dicker-Fuller test seasonality white noise random walk autoregression moving average ACF and PACF, Model selection with AIC (Akaike's Information Criterion) Then, we move on and apply more complex statistical models for time series forecasting: ARIMA (Autoregressive Integrated Moving Average model) SARIMA (Seasonal Autoregressive Integrated Moving Average model) SARIMAX (Seasonal Autoregressive Integrated Moving Average model with exogenous variables) We also cover multiple time series forecasting with: VAR (Vector Autoregression) VARMA (Vector Autoregressive Moving Average model) VARMAX (Vector Autoregressive Moving Average model with exogenous variable) Then, we move on to the deep learning section, where we will use Tensorflow to apply different deep learning techniques for times series analysis: Simple linear model (1 layer neural network) DNN (Deep Neural Network) CNN (Convolutional Neural Network) LSTM (Long Short-Term Memory) CNN + LSTM models ResNet (Residual Networks) Autoregressive LSTM Throughout the course, you will complete more than 5 end-to-end projects in Python, with all source code available to you. | https://www.udemy.com/course/applied-time-series-analysis-in-python/#instructor-1 | Experience as a data scientist I completed a bachelor degree in a field that did not interest me. Instead, I started learning web development on the side and landed my first job as a web developer. I went on to teach myself data science, as I was very curious about the idea of machines learning by themselves. I proceeded to land another job as a professional data scientist, even though I do not have a masters or a PhD. As a self-taught data scientist and web developer, I know what it feels like to dive in a completely new field. I know the hard parts, and I know what must be taught to land a professional job and gain new skills with a real impact on our career. Experience as an instructor As far I can remember, I was always the person explaining to my peers. Through tutoring, blog articles, and courses, I have a passion for sharing my knowledge and teaching. I strive to have an impact on my students and see them become better and more knowledgeable. | Python | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Mining with R: Go from Beginner to Advanced! | Learn to use R software for data analysis, visualization, and to perform dozens of popular data mining techniques. | 4.6 | 401 | 4499 | Created by Geoffrey Hubona, Ph.D. | Aug-20 | English | $11.99 | 11h 54m total length | https://www.udemy.com/course/data-mining-with-r-go-from-beginner-to-advanced/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases. There are also "hands-on" exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities. | https://www.udemy.com/course/data-mining-with-r-go-from-beginner-to-advanced/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
R Programming for Simulation and Monte Carlo Methods | Learn to program statistical applications and Monte Carlo simulations with numerous "real-life" cases and R software. | 4.5 | 397 | 4235 | Created by Geoffrey Hubona, Ph.D. | Jul-20 | English | $9.99 | 11h 42m total length | https://www.udemy.com/course/r-programming-for-simulation-and-monte-carlo-methods/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | R Programming for Simulation and Monte Carlo Methods focuses on using R software to program probabilistic simulations, often called Monte Carlo Simulations. Typical simplified "real-world" examples include simulating the probabilities of a baseball player having a 'streak' of twenty sequential season games with 'hits-at-bat' or estimating the likely total number of taxicabs in a strange city when one observes a certain sequence of numbered cabs pass a particular street corner over a 60 minute period. In addition to detailing half a dozen (sometimes amusing) 'real-world' extended example applications, the course also explains in detail how to use existing R functions, and how to write your own R functions, to perform simulated inference estimates, including likelihoods and confidence intervals, and other cases of stochastic simulation. Techniques to use R to generate different characteristics of various families of random variables are explained in detail. The course teaches skills to implement various approaches to simulate continuous and discrete random variable probability distribution functions, parameter estimation, Monte-Carlo Integration, and variance reduction techniques. The course partially utilizes the Comprehensive R Archive Network (CRAN) spuRs package to demonstrate how to structure and write programs to accomplish mathematical and probabilistic simulations using R statistical software. | https://www.udemy.com/course/r-programming-for-simulation-and-monte-carlo-methods/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Advanced Web Scraping with Python using Scrapy & Splash | The most advanced web scraping & crawling course using Scrapy & Splash! Take your web scraping skills to the next level. | 4.6 | 397 | 5715 | Created by Ahmed Rafik | Aug-20 | English | $9.99 | 5h 35m total length | https://www.udemy.com/course/advanced-web-scraping-with-python-using-scrapy-splash/ | Ahmed Rafik | Developer and Online Instructor | 4.4 | 4223 | 30396 | Hi there & welcome to the most advanced online resource on Web Scraping with Python using Scrapy & Splash. This course is fully project-based means pretty much on each section we gonna scrape a different website & tackle a different web scraping dilemma also rather than focusing on the basics of Scrapy & Splash we gonna dive straight forward into real-world projects, this also means that this course is absolutely not suitable for beginners with no background on web scraping, Scrapy, Splash & XPath expressions. ---This courses covers a variety of topics such as:--- Requests chaining, like how the requests must be sent in a certain order otherwise they won't be fulfilled at all. How to analyze a website before scraping it, this is an important step to do since it helps a lot in choosing the right tools to scrape a website & it literally has a huge impact on the performance of your final product. How to optimize Splash scripts by reducing/aborting all the unnecessary requests that have nothing to do with the data points you're going to scrape, this is an important thing to do if you care about the performance of Splash as it is the key to bypass 504 Gateway Timeout HTTP errors in Splash. We gonna also cover how to build a Cluster of Splash instances with a load balancer(HAProxy) rather than having one fully overloaded Splash instance this also helps in bypassing 504 Gateway Timeout errors. Heavy data processing, you'll understand how Input & Output processors work so you'll be able to use them in order to clean the scraped data points as this will ensure the quality of your feeds. We'll use ScrapyRT (Scrapy RealTime) to build spiders that can fetch data in real-time. Showcase the scraped data points in a minimalist web app using ScrapyRT & Flask, this is extremely helpful for web scraping freelancers. Bypass Google ReCaptcha, please don't get me wrong on this point, I don't mean that we will solve it using Scrapy, instead, I'm gonna show you a technique that I use frequently to fool websites and let them think that the request is sent using a browser & was performed by a human being! Build clean & well-structured spiders Finally, we gonna build a Desktop app using Tkinter, the app will fetch & execute all the available spiders in your Scrapy project, you can also choose the feed type, feed location & name, this is also extremely helpful & important if you're a web scraping freelancer, it is always a good idea to deliver to your client a desktop app rather than installing Scrapy on his machine & stuff like that. This course is straight to the point, there's no "foobar" or "quotes to toscrape dot com" as other courses do so make sure you have a good level of focus & lot of determination & motivation. By the end of this course, you'll sharpen your skills in web scraping using Scrapy & Splash, you'll be able to write clean & high performing spiders that differentiate you from others, this also means if you're a web scraping freelancer you'll get more offers since you can deliver "User-Friendly" spiders with a Graphical User Interface(GUI) or web apps that fetch data in real-time. So join me on this course & let's harvest the web together! | https://www.udemy.com/course/advanced-web-scraping-with-python-using-scrapy-splash/#instructor-1 | Who I am? I’m Ahmed Rafik, I'm a self-taught developer & an online teacher on Udemy. I've helped thousands of people learning web scraping with Python using different tools such as Scrapy, Splash & Selenium. As a self-taught developer, I found myself jumping between different tutorials and (e-)books trying to understand how things can work out together, I was literally wasting so much time trying to connect the dots rather than learning how to code and over time this has become so overwhelming with lots of things to learn and to connect. I believe coding should be easy for everyone but this also requires you to choose the right instructor with the right knowledge. In my courses, I'm gonna teach you the skills you need to start your web scraping career from the get-go with no fluff. I try as much as possible to avoid the boring theoretical explanations unless it's necessary, I always keep my courses up to date and that's what helped me to have the highest-rated and the best selling web scraping courses on Udemy. I can't wait to see you enrolled in one of my courses, I'll make sure to be there for you on every step you make and answer any questions you have. | Python | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Spatial Analysis & Geospatial Data Science in Python | Learn how to process and visualize geospatial data and perform spatial analysis using Python. | 4.4 | 387 | 51220 | Created by Shan Singh | Nov-22 | English | $9.99 | 3h 57m total length | https://www.udemy.com/course/spatial-data-science-in-python/ | Shan Singh | Top Rated & Best-Selling Udemy Instructor , Data Scientist | 4.4 | 5625 | 284385 | Geospatial data science is a subset of data science that focuses on spatial data and its unique techniques. In this, we are going to perform spatial analysis and trying to find insights from spatial data. In this course, we lay the foundation for a career in Geospatial Data Science. You will get hands-on Geopy, Plotly etc.. the workhorse of Geospatial data science Python libraries. The topics covered in this course widely touch on some of the most used spatial technique in Geospatial data science. We will be learning how to read spatial data , manipulate and process spatial data using Pandas , and perform some spatial operations. A large portion of the course deals with spatial Visuals like Choropleth, Geographical Scatter plot, Geographical Heatmap, Markers, Geographical HeatMap. Each video contains a summary of the topic and a walkthrough with code examples that will help you learn more effectively. Who this course is for: Students who want to become Data Scientist by show-case these Projects on his/her Resume.. Students who like to take their first steps in the Geospatial data science career. Python users who are interested in Spatial Data Science. GIS users who are new to python and Jupyter notebooks for Geographic data analysis... | https://www.udemy.com/course/spatial-data-science-in-python/#instructor-1 | great courses for beginners/working Professionals If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Whether it’s a Data Science , Data Analysis ,Machine Learning , Time Series or Natural Language Processing skills and more here. Query resolution 24*7 One-on-one support from experts that truly want to help you Query resolution (QnA) - Within 2-3 hours in day time Hardly it can be 8-10 hours.. learn by doing Step-by-step tutorials and project-based learning. more about Shan Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you! | Python | Data Scientist | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Testing and Monitoring Machine Learning Model Deployments | ML testing strategies, shadow deployments, production model monitoring and more | 4.4 | 384 | 5059 | Created by Christopher Samiullah, Soledad Galli | Jun-22 | English | $11.99 | 8h 19m total length | https://www.udemy.com/course/testing-and-monitoring-machine-learning-model-deployments/ | Christopher Samiullah | Machine Learning Engineer | 4.4 | 4689 | 28585 | Learn how to test & monitor production machine learning models. What is model testing? You’ve taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren’t any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk. What is model monitoring? You’ve deployed your model to production. OK now what? Is it working as you expect? How do you know? By monitoring models, we can check for unexpected changes in: Incoming data Model quality System operations When we think about data science, we think about how to build machine learning models, which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate. However, how we are going to actually test & monitor these models in a production system is often neglected, . Only when we can effectively monitor our production models can we determine if they are performing as we expect. Why take this course? This is the first and only online course where you can learn how to test & monitor machine learning models. The course is comprehensive, and yet easy to follow. Throughout this course you will learn all the steps and techniques required to effectively test & monitor machine learning models professionally. In this course, you will have at your fingertips the sequence of steps that you need to follow to test & monitor a machine learning model, plus a project template with full code, that you can adapt to your own models. What is the course structure? Part 1: Testing The course begins from the most common starting point for the majority of data scientists: a Jupyter notebook with a machine learning model trained in it. We gradually build up the complexity, testing the model first in the Juyter notebook and then in a realistic production code base. Hands-on exercises are interspaced with relevant and actionable theory. Part 2: Shadow Mode We explain the theory & purpose of deploying a model in shadow mode to minimize your risk, and walk you through an example project setup. Part 3: Monitoring We take you through the theory & practical application of monitoring metrics & logs for ML systems. Important: This course does not cover model deployment (we have a separate course dedicated to that topic) Who are the instructors? We have gathered a fantastic team to teach this course. Sole is a leading data scientist in finance and insurance, with 3+ years of experience in building and implementing machine learning models in the field, and multiple IT awards and nominations. Chris is a tech lead & ML software engineer with enormous experience in building APIs and deploying machine learning models, allowing business to extract full benefit from their implementation and decisions. Who is this course for? Data Scientists who want to know how to test & monitor their models beyond in production Software engineers who want to learn about Machine Learning engineering Machine Learning engineers who want to improve their testing & monitoring skills Data Engineers looking to transition to ML engineering Lovers of open source technologies How advanced is this course? This is an advanced level course, and it requires you to have experience with Python programming and git. How much experience? It depends on how much time you would like to set aside to go ahead and learn those concepts that are new to you. To give you an example, we will work with Python environments, we will work with object oriented programming, we will work with the command line to run our scripts, and we will checkout code at different stages with git. You don’t need to be an expert in all of these topics, but you need a reasonable working knowledge. We also work with Docker a lot, though we will provide a recap of this tool. For those relatively new to software engineering, the course will be challenging. We have added detailed lecture notes and references, so we believe that those missing some of the prerequisites can take the course, but keep in mind that you will need to put in the hours to read up on unfamiliar concepts. On this point, the course slowly increases in complexity, so you can see how we pass, gradually, from the familiar Jupyter notebook, to the less familiar production code, using a project-based approach which we believe is optimal for learning. It is important that you follow the code, as we gradually build it up. Still not sure if this is the right course for you? Here are some rough guidelines: Never written a line of code before: This course is unsuitable Never written a line of Python before: This course is unsuitable Never trained a machine learning model before: This course is unsuitable. Ideally, you have already built a few machine learning models, either at work, or for competitions or as a hobby. Never used docker before: The second part of the course will be very challenging. You need to be ready to read up on lecture notes & references. Have only ever operated in the research environment: This course will be challenging, but if you are ready to read up on some of the concepts we will show you, the course will offer you a great deal of value. Have a little experience writing production code: There may be some unfamiliar tools which we will show you, but generally you should get a lot from the course. Non-technical: You may get a lot from just the theory lectures, so that you get a feel for the challenges of ML testing & monitoring, as well as the lifecycle of ML models. The rest of the course will be a stretch. To sum up: With more than 70 lectures and 8 hours of video this comprehensive course covers every aspect of model testing & monitoring. Throughout the course you will use Python as your main language and other open source technologies that will allow you to host and make calls to your machine learning models. We hope you enjoy it and we look forward to seeing you on board! | https://www.udemy.com/course/testing-and-monitoring-machine-learning-model-deployments/#instructor-1 | My name is Chris. I'm a professional software engineer from the UK. I've been writing code for over a decade, and for the past five years I've focused on scaling machine learning applications. I've done this at fintech and healthtech companies in London, where I've worked on and grown production machine learning applications used by millions of people. I've built and maintained machine learning systems which make credit-risk and fraud detection judgements on over a billion dollars of personal loans per year for the challenger bank Zopa. I previously worked on systems for predicting health risks for patients around the world at Babylon Health. In the past, I've worn a variety of hats. I worked at a global healthcare company, Bupa, which included being a core developer on their flagship website, and three years working in Beijing setting up mobile, web and IT for medical centers in China. Whilst in Beijing, I ran the Python meetup group, mentored a lot of junior developers, and ate a lot of dumplings. I enjoy giving talks at engineering meetups, building systems that create value, and writing software development tutorials and guides. I've written on topics ranging from wearable development, to internet security, to Python web frameworks. I'm passionate about teaching in a way that minimizes the time between "ah hah" moments, but doesn't leave you Googling every other word. Complexity is necessary for application in the real world, but too much complexity is overwhelming and counter-productive. I will help you find the right balance. Feel free to connect on LinkedIn (very active) or Twitter (getting more active in 2022) | Machine Learning | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Reinforcement Learning with Pytorch | Learn to apply Reinforcement Learning and Artificial Intelligence algorithms using Python, Pytorch and OpenAI Gym | 4.6 | 382 | 2562 | Created by Atamai AI Team | Aug-20 | English | $13.99 | 7h 14m total length | https://www.udemy.com/course/reinforcement-learning-with-pytorch/ | Atamai AI Team | Data Science & AI Passion | 4.6 | 382 | 2562 | UPDATE: All the code and installation instructions have been updated and verified to work with Pytorch 1.6 !! Artificial Intelligence is dynamically edging its way into our lives. It is already broadly available and we use it - sometimes even not knowing it - on daily basis. Soon it will be our permanent, every day companion. And where can we place Reinforcement Learning in AI world? Definitely this is one of the most promising and fastest growing technologies that can eventually lead us to General Artificial Intelligence! We can see multiple examples where AI can achieve amazing results - from reaching super human level while playing games to solving real life problems (robotics, healthcare, etc). Without a doubt it's worth to know and understand it! And that's why this course has been created. We will go through multiple topics, focusing on most important and practical details. We will start from very basic information, gradually building our understanding, and finally reaching the point where we will make our agent learn in human-like way - only from video input! What's important - of course we need to cover some theory - but we will mainly focus on practical part. Goal is to understand WHY and HOW. In order to evaluate our algorithms we will use environments from - very popular - OpenAI Gym. We will start from basic text games, through more complex ones, up to challenging Atari games What will be covered during the course ? - Introduction to Reinforcement Learning - Markov Decision Process - Deterministic and stochastic environments - Bellman Equation - Q Learning - Exploration vs Exploitation - Scaling up - Neural Networks as function approximators - Deep Reinforcement Learning - DQN - Improvements to DQN - Learning from video input - Reproducing some of most popular RL solutions - Tuning parameters and general recommendations See you in the class! | https://www.udemy.com/course/reinforcement-learning-with-pytorch/#instructor-1 | We are independent AI researchers, working with Artificial Intelligence and Deep Learning projects on daily basis. We are absolutely passionate about it and we want to share this passion with you. We're also experienced instructors (mainly doing in person trainings so far) and we simply love sharing our knowledge with others! We're looking forward to see you in one of our courses! | PyTorch | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Face Recognition Web App with Machine Learning in Flask | Create an Face Recognition (AI) project from scratch with Python, OpenCV , Machine Learning Algorithms, Flask and Deploy | 4.2 | 376 | 23446 | Created by Sudhir G, Data Science Anywhere | Sep-22 | English | $9.99 | 10h 35m total length | https://www.udemy.com/course/build-face-recognition-app-using-machine-learning-in-flask/ | Sudhir G | Data Scientist | 4.3 | 686 | 73453 | Face Recognition Web Project using Machine Learning in Flask Python Face recognition is one of the most widely used in my application. If at all you want to develop and deploy the application on the web only knowledge of machine learning or deep learning is not enough. You also need to know the creation of pipeline architecture and call it from the client-side, HTTP request, and many more. While doing so you might face many challenges while developing the app. This course is structured in such a way that you can able to develop the face recognition based web app from scratch. What you will learn? Python Image Processing with OpenCV Image Data Preprocessing Image Data Analysis Eigenfaces with PCA Face Recognition Classification Model with Support Vector Machines Pipeline Model Flask (Jinja Template, HTML, CSS, HTTP Methods) Develop Face Recognition Web Deploy Flask App in Cloud You will learn image processing techniques in OpenCV and the concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for images. For the preprocess images, we will extract features from the images, ie. computing Eigen images using principal component analysis. With Eigen images, we will train the Machine learning model and also learn to test our model before deploying, to get the best results from the model we will tune with the Grid search method for the best hyperparameters. Once our machine learning model is ready, will we learn and develop a web server gateway interphase in flask by rendering HTML CSS and bootstrap in the frontend and in the backend written in Python. Finally, we will create the project on the Face Recognition project by integrating the machine learning model to Flask App. | https://www.udemy.com/course/build-face-recognition-app-using-machine-learning-in-flask/#instructor-1 | Sudhir is an experienced Data Scientist with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Statistical algorithms he mostly worked on Image Processing and Natural Language processing application. He also successfully deployed many data science-related projects in cloud platforms as a service. Strong engineering professional with a Bachelor's degree focused on Electrical and Electronics Engineering. | Machine Learning | Data Scientist | >=4 | Below 1K | >=20K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Learn By Example: Statistics and Data Science in R | A gentle yet thorough introduction to Data Science, Statistics and R using real life examples | 3.7 | 376 | 4866 | Created by Loony Corn | Dec-16 | English | $9.99 | 9h 7m total length | https://www.udemy.com/course/statistics-and-data-science-in-r/ | Loony Corn | An ex-Google, Stanford and Flipkart team | 4.2 | 26022 | 153513 | Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a gentle yet thorough introduction to Data Science, Statistics and R using real life examples. Let’s parse that. Gentle, yet thorough: This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median etc and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualising your findings. Data Science, Statistics and R: This course is an introduction to Data Science and Statistics using the R programming language. It covers both the theoretical aspects of Statistical concepts and the practical implementation using R. Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. What's Covered: Data Analysis with R: Datatypes and Data structures in R, Vectors, Arrays, Matrices, Lists, Data Frames, Reading data from files, Aggregating, Sorting & Merging Data Frames Linear Regression: Regression, Simple Linear Regression in Excel, Simple Linear Regression in R, Multiple Linear Regression in R, Categorical variables in regression, Robust regression, Parsing regression diagnostic plots Data Visualization in R: Line plot, Scatter plot, Bar plot, Histogram, Scatterplot matrix, Heat map, Packages for Data Visualisation : Rcolorbrewer, ggplot2 Descriptive Statistics: Mean, Median, Mode, IQR, Standard Deviation, Frequency Distributions, Histograms, Boxplots Inferential Statistics: Random Variables, Probability Distributions, Uniform Distribution, Normal Distribution, Sampling, Sampling Distribution, Hypothesis testing, Test statistic, Test of significance | https://www.udemy.com/course/statistics-and-data-science-in-r/#instructor-1 | Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you'll like them 🙂 | Statistics | Yes | >=3 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Natural Language Processing (NLP) in Python with 8 Projects | Work on 8 Projects, Learn Natural Language Processing Python, Machine Learning, Deep Learning, SpaCy, NLTK, Sklearn, CNN | Bestseller | 4.3 | 377 | 3845 | Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy | Nov-22 | English | $13.99 | 10h 25m total length | https://www.udemy.com/course/complete-natural-language-processing-nlp-with-spacy-nltk/ | Ankit Mistry | Software Developer | I want to Improve your life & Income. | 4.4 | 6134 | 82664 | Recent reviews: "Thorough explanation, going great so far. A very simplistic and straightforward introduction to Natural Language Processing. I will recommend this class to any one looking towards Data Science" "This course so far is breaking down the content into smart bite-size pieces and the professor explains everything patiently and gives just enough background so that I do not feel lost." "This course is really good for me. it is easy to understand and it covers a wide range of NLP topics from the basics, machine learning to Deep Learning. The codes used is practical and useful. I definitely satisfy with the content and surely recommend to everyone who is interested in Natural Language Processing" ------------------------------------------------------------------------------------------------------------------------------------------------------ Update 1.0 : Fasttext Library for Text classification section added. ------------------------------------------------------------------------------------------------------------------------------------------------------ Hi Data Lovers, Do you have idea about Which Artificial Intelligence field is going to get big in upcoming year? According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is 'Natural Language Processing', You are at right place. ----------------------------------------------------------------------------------------------------------------------------------------------------- Do you want to know How Google News classify millions of news article into hundreds of different category. How Android speech recognition recognize your voice with such high accuracy. How Google Translate actually translate hundreds of pairs of different languages into one another. If answer is "Yes", You are on right track. and to help yourself, me and my friend Vijay have created comprehensive course For Students and Professionals to learn Natural Language Processing from very Beginning ----------------------------------------------------------------------------------------------------------------------------------------------------- NLP - "Natural Language Processing" has found space in every aspect of our daily life. Cell phone internet are the integral part of our life. Any most application you will find the use of NLP methods, from search engine of Google to recommendation system of Amazon & Netflix. Chat-bot Google Now, Apple Siri, Amazon Alexa Machine Translation Sentiment analysis Speech Recognition and many more. So, welcome to my course on NLP. Natural Language Processing (NLP) in Python with 8 Projects ----------------------------------------------------------------------------------------------------------------------------------------------------- This course has 10+ Hours of HD Quality video, and following content. Course Outline : 1 : Welcome In this section we will get complete idea about what we are going to learn in the whole course and understanding related to natural language processing. 2 : Installation & Setup In this section we will get our online environment Google Colab setup. 3 : Basics of Natural Language Processing In this section we will dive into all basic NLP task like Tokenization, Lemmatization, stop word removal, name entity recognition, part of speech tagging, and see how to apply with different functions available in a Spacy and NLTK library. 4, 5, 6 : Spam Message Classification, Restaurant Review Prediction (Good or bad), IMDB, Amazon and Yelp review Classification In the next 3 section we will get dive into a real world data set for text classification, spam detection, restaurant review classification, Amazon IMDb reviews. We will see how to do Pre-Processing and make your data suitable for machine learning algorithm and apply different Machine Learning estimator (Logistic Regression, SVM, Decision Tree) for classifying text. 7, 8 : Automated Text Summarization, Twitter sentiment Analysis In this 2 section we will work upon real world application of NLP. Automatic text summarisation, Which compress your text to find the summary of big articles Another one we will work is finding the sentiment from the recently posted tweet about some specific keyword with the help of Twitter API - tweepy library 9 : Deep Learning Basics In This Section we will get a basic idea about Deep learning concept, like artificial neural network activation function and how ANN works. 10 : Word Embedding In This Section, we will see How to implement word2vec on our custom datasets, as well as using Pretrained Google Model. 11, 12 : Text Classification with CNN & RNN In this section we will see how to apply advanced deep learning model like convolution neural networks and recurrent neural networks for text classification. 13 : Automatic Text Generation using TensorFlow, Keras and LSTM In this section we will apply neural network based LSTM model to automatically generate text. 14, 15, 16, 17 : Numpy, Pandas, Matplotlib + File Processing In this section, for all of you who want refresh concept related to data analysis with Numpy and Pandas library, Data Visualization with Matplotlib library, and Text File processing and PDF File processing. ----------------------------------------------------------------------------------------------------------------------------------------------------- So, This is the one of the most comprehensive course on natural language processing, And I am expecting you to know basic knowledge of python and your curiosity to learn Different techniques in NLP world. YOU'LL ALSO GET: Lifetime access to Natural Language Processing (NLP) with Python Course Udemy Certificate of Completion available for download Friendly support in the Q&A section So What Are You Waiting For ? Enroll today! and Empower Your Career ! I can't wait for you to get started on mastering NLP with Python. Start analyzing your text data & I will see you inside a class. Regards Ankit & Vijay | https://www.udemy.com/course/complete-natural-language-processing-nlp-with-spacy-nltk/#instructor-1 | I am Ankit Mistry, completed my master from IIT Kharagpur in area of machine learning, Artificial intelligence. Now working as Software Developer, Big Data Engineer in one of leading private investment bank with 8+ years of experience in software industry. Over the time I developed interest related to data discipline and learned about data analysis, machine learning model development, Cloud Computing. Created course in area of Cloud Computing, Google Cloud, Python, Data Science, Data analysis, Machine Learning. I am so excited to be on Udemy online learning platform and want to make big impact on your software career. I hope you will like my course offering. | NLP | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Statistics Made Easy by Example for Analytics/ data science | Statistics Simplified - Statistics Made Easy by Excel Simulations. Master fundamentals of statistics & Probability. | 4.3 | 374 | 2397 | Created by Gopal Prasad Malakar | Jan-21 | English | $9.99 | 15h 41m total length | https://www.udemy.com/course/statistics-by-example/ | Gopal Prasad Malakar | Trains Industry Practices on data science / machine learning | 4.2 | 10737 | 111703 | What is the course about? This course promises that students will Learn the statistics in a simple and interesting way Know the business scenarios, where it is applied See the demonstration of important concepts (simulations) in MS Excel Practice it in MS Excel to cement the learning Get confidence to answer questions on statistics Be ready to do more advance course like logistic regression etc. Course Material The course comprises of primarily video lectures. All Excel file used in the course are available for download. The complete content of the course is available to download in PDF format. How long the course should take? It should take approximately 25 hours for good grasp on the subject. Why take the course To understand statistics with ease Get crystal clear understanding of applicability Understand the subject with the context See the simulation before learning the theory | https://www.udemy.com/course/statistics-by-example/#instructor-1 | I am a seasoned Analytics professional with 20+ years of professional experience. I have industry experience of impactful and actionable analytics, data science, decision strategy and enterprise wise data strategy. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting, business intelligence systems like tableau /SAS Visual Analytics, MS access based database application development, Enterprise wide big data framework and streaming analysis. Please refer to my course for - SAS / R program details (syntax and options) - SAS / R output deep dive - Practical usage in Industrial situation | Statistics | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1 Lakh | |||||||||||||||||
Modern Reinforcement Learning: Actor-Critic Algorithms | How to Implement Cutting Edge Artificial Intelligence Research Papers in the Open AI Gym Using the PyTorch Framework | 4.1 | 369 | 2412 | Created by Phil Tabor | Oct-20 | English | $11.99 | 8h 10m total length | https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/ | Phil Tabor | Machine Learning Engineer | 4.5 | 1271 | 5586 | In this advanced course on deep reinforcement learning, you will learn how to implement policy gradient, actor critic, deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and soft actor critic (SAC) algorithms in a variety of challenging environments from the Open AI gym. There will be a strong focus on dealing with environments with continuous action spaces, which is of particular interest for those looking to do research into robotic control with deep reinforcement learning. Rather than being a course that spoon feeds the student, here you are going to learn to read deep reinforcement learning research papers on your own, and implement them from scratch. You will learn a repeatable framework for quickly implementing the algorithms in advanced research papers. Mastering the content in this course will be a quantum leap in your capabilities as an artificial intelligence engineer, and will put you in a league of your own among students who are reliant on others to break down complex ideas for them. Fear not, if it's been a while since your last reinforcement learning course, we will begin with a briskly paced review of core topics. The course begins with a practical review of the fundamentals of reinforcement learning, including topics such as: The Bellman Equation Markov Decision Processes Monte Carlo Prediction Monte Carlo Control Temporal Difference Prediction TD(0) Temporal Difference Control with Q Learning And moves straight into coding up our first agent: a blackjack playing artificial intelligence. From there we will progress to teaching an agent to balance the cart pole using Q learning. After mastering the fundamentals, the pace quickens, and we move straight into an introduction to policy gradient methods. We cover the REINFORCE algorithm, and use it to teach an artificial intelligence to land on the moon in the lunar lander environment from the Open AI gym. Next we progress to coding up the one step actor critic algorithm, to again beat the lunar lander. With the fundamentals out of the way, we move on to our harder projects: implementing deep reinforcement learning research papers. We will start with Deep Deterministic Policy Gradients (DDPG), which is an algorithm for teaching robots to excel at a variety of continuous control tasks. DDPG combines many of the advances of Deep Q Learning with traditional actor critic methods to achieve state of the art results in environments with continuous action spaces. Next, we implement a state of the art artificial intelligence algorithm: Twin Delayed Deep Deterministic Policy Gradients (TD3). This algorithm sets a new benchmark for performance in continuous robotic control tasks, and we will demonstrate world class performance in the Bipedal Walker environment from the Open AI gym. TD3 is based on the DDPG algorithm, but addresses a number of approximation issues that result in poor performance in DDPG and other actor critic algorithms. Finally, we will implement the soft actor critic algorithm (SAC). SAC approaches deep reinforcement learning from a totally different angle: by considering entropy maximization, rather than score maximization, as a viable objective. This results in increased exploration by our agent, and world class performance in a number of important Open AI Gym environments. By the end of the course, you will know the answers to the following fundamental questions in Actor-Critic methods: Why should we bother with actor critic methods when deep Q learning is so successful? Can the advances in deep Q learning be used in other fields of reinforcement learning? How can we solve the explore-exploit dilemma with a deterministic policy? How do we get and deal with overestimation bias in actor-critic methods? How do we deal with the inherent approximation errors in deep neural networks? This course is for the highly motivated and advanced student. To succeed, you must have prior course work in all the following topics: College level calculus Reinforcement learning Deep learning The pace of the course is brisk and the topics are at the cutting edge of deep reinforcement learning research, but the payoff is that you will come out knowing how to read research papers and turn them into functional code as quickly as possible. You'll never have to rely on dodgy medium blog posts again. | https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/#instructor-1 | In 2012 I received my PhD in experimental condensed matter physics from West Virginia University. Following that I was a dry etch process engineer for Intel Corporation, where I leveraged big data to make essential process improvements for mission critical products. After leaving Intel in 2015, I have worked as a contract and freelance deep learning and artificial intelligence engineer. | Misc | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Computer Vision: Python OCR & Object Detection Quick Starter | Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python | 4.3 | 366 | 4548 | Created by Abhilash Nelson | Apr-22 | English | $11.99 | 4h 41m total length | https://www.udemy.com/course/computer-vision-python-ocr-object-detection-quick-starter/ | Abhilash Nelson | Computer Engineering Master & Senior Programmer at Dubai | 4.2 | 2186 | 41457 | Hi There! welcome to my new course 'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars. This course will be a quick starter for people who wants to dive into Optical Character Recognition, Image Recognition and Object Detection using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process. Let's now see the list of interesting topics that are included in this course. At first we will have an introductory theory session about Optical Character Recognition technology. After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine. Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures. Then we will install the dependencies and libraries that we require to do the Optical Character Recognition. We are using Tesseract Library to do the OCR. At first we will install the Library and then its python bindings. We will also install OpenCV, which is the Open Source Computer Vision library in Python. We also will install the Pillow library, which is the Python Image Library. Then we will have an introduction to the steps involved in the Optical Character Recognition and later will proceed with coding and implementing the OCR program. We will use few example images to do a Character Recognition testing and will verify the results. Then we will have an introduction to Convolutional Neural Networks , which we will be using to do the Image Recognition. Here we will be classifying a full image based on the single primary object in it. We will then proceed with installing the Keras Library which we will be using to do the Image recognition. We will be using the built in , pre-trained Models that are included in Keras. The base code in python is also provided in the Keras documentation. At first We will be using the popular pre-trained model architecture called the VGGNet. we will have an introductory session about the architecture of VGGNet. Then we will proceed with using the pre-trained VGGNet 16 Model included in keras to do Image Recognition and classification. We will try with few sample images to check the predictions. Then will move on to a deeper VGGNet 19 Model included in keras to do Image Recognition and classification. Then we will try the ResNet pre-trained model included with the Keras library. We will include the model in the code and then we will try with few sample images to check the predictions. And after that we will try the Inception pre-trained model. We will also include the model in the code and then we will try with few sample images to check the predictions. Then will go ahead with the Xception pre-trained model. Here also, we will include the model in the code and then we will try with few sample images. And those were Image Recognition pre-trained models, which can only label and classify a complete image based on the primary object in it. Now we will proceed with Object Recognition in which we can detect and label multiple objects in a single image. At first we will have an introduction to MobileNet-SSD Pre-trained Model, which is single shot detector that is capable of detecting multiple objects in a scene. We will be also be having a quick discussion about the dataset that is used to train this model. Later we will be implementing the MobileNet-SSD Pre-trained Model in our code and will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value. Then we will go ahead with object detection from a live video. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw rectangle around each object detected in the live video along with the label and confidence. In the next session, we will go ahead with object detection from a pre-saved video. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw rectangle around each object detected along with the label and confidence. Later we will be going ahead with the Mask-RCNN Pre-trained Model. In the previous model, we were only able to get a bounding box around the object, but in Mask-RCNN, we can get both the box co-ordinates as well the mask over the exact shape of object detected. We will have an introduction about this model and its details. Later we will be implementing the Mask-RCNN Pre-trained Model in our code and as the first step we will get the predictions and bounding box coordinates for every object detected. We will draw the bounding box around the objects in the image and write the label along with the confidence value. Later we will be getting the mask returned for each object predicted. We will process that data and use it to draw translucent multi coloured masks over each and every object detected and write the label along with the confidence value. Then we will go ahead with object detection from a live video using Mask-RCNN. We will be streaming the real-time live video from the computer's webcam and will try to detect objects from it. We will draw the mask over the perimeter of each object detected in the live video along with the label and confidence. And like we did for our previous model, we will go ahead with object detection from a pre-saved video using Mask-RCNN. We will be streaming the saved video from our folder and will try to detect objects from it. We will draw coloured masks for object detected along with the label and confidence. The Mask-RCNN is very accurate with vast class list but will be very slow in processing images using low power CPU based computers. MobileNet-SSD is fast but less accurate and low in number of classes. We need a perfect blend of speed and accuracy which will take us to Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image. And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file. To further improve the speed of frames processed, we will use the model called Tiny YOLO which is a light weight version of the actual yolo model. We will use tiny yolo at first for the pre-saved video and will analyse the accuracy as well as speed and then we will try the same for a real-time video from webcam and see the difference in performance compared to actual yolo. That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio. So that's all for now, see you soon in the class room. Happy learning and have a great time. | https://www.udemy.com/course/computer-vision-python-ocr-object-detection-quick-starter/#instructor-1 | I am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design. | Computer Vision | Senior Role | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Apache Airflow: Complete Hands-On Beginner to Advanced Class | Learn Apache Airflow step-by-step. Real-Life Data Pipelines & Quizzes Included. Learn by Doing! | 3.4 | 366 | 2048 | Created by Alexandra Abbas | Sep-20 | English | $11.99 | 4h 55m total length | https://www.udemy.com/course/apache-airflow-course/ | Alexandra Abbas | Google Cloud Certified Data Engineer & Architect | 3.3 | 422 | 2193 | Hi there, my name is Alexandra Abbas. I’m an Apache Airflow Contributor and a Google Cloud Certified Data Engineer & Architect with over 3 years experience as a Data Engineer. Are you struggling to learn Apache Airflow on your own? In this course I will teach you Airflow in a practical manner, with every lecture comes a full coding screencast. By the end of the course you will be able to use Airflow professionally and add Airflow to your CV. This course includes 50 lectures and more than 4 hours of video, quizzes, coding exercises as well as 2 major real-life projects that you can add to your Github portfolio! You will learn: How to install and set up Airflow on your machine Basic and advanced Airflow concepts How to develop complex real-life data pipelines How to interact with Google Cloud from your Airflow instance How to extend Airflow with custom operators and sensors How to test Airflow pipelines and operators How to monitor your Airflow instance using Prometheus and Grafana How to track errors with Sentry How to set up and run Airflow in production This course is for beginners. You do not need any previous knowledge of Apache Airflow, Data Engineering or Google Cloud. We will start right at the beginning and work our way through step by step. You will get lifetime access to over 50 lectures plus corresponding cheat sheets, datasets and code base for the lectures! | https://www.udemy.com/course/apache-airflow-course/#instructor-1 | Alexandra is a Google Cloud Certified Data Engineer & Architect and Apache Airflow Contributor. She has experience with large-scale data science and engineering projects. She spends her time building data pipelines using Apache Airflow and Apache Beam and creating production ready Machine Learning pipelines with Tensorflow. Alexandra was a speaker at Serverless Days London 2019 and presented at the Tensorflow London meetup. | Misc | Architect | >=3 | Below 1K | Below 10K | >=3 | Below 1 K | Below 10 K | |||||||||||||||||
NLP Course for Beginner | Learn Natural Language Processing ( NLP ) & how to analyze text data. | 4.1 | 365 | 63855 | Created by Code Warriors, Mayank Bajaj | Jul-20 | English | $9.99 | 1h 6m total length | https://www.udemy.com/course/nlp-course-for-beginner/ | Code Warriors | The best place to learn, code and conquer - Once you have it | 4 | 4869 | 305419 | Welcome to the best Natural Language Processing course on the Udemy! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text. We'll understand fundamental NLP concepts such as stemming, lemmatization, stop words, tokenization and more! Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems. We'll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information. Through state of the art visualization libraries we will be able view these relationships in real time. Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages. We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files. | https://www.udemy.com/course/nlp-course-for-beginner/#instructor-1 | Hi, We are Code Warriors an E learning organisation . This is our Udemy Handle where we will provide you some awesome courses with very basic price. The courses will be very much informative and you will enjoy a lot. We focus on your learning in an enjoying manner so you don't get bored. | NLP | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Mastering Databricks & Apache spark -Build ETL data pipeline | Learn fundamental concept about databricks and process big data by building your first data pipeline on Azure | 4 | 363 | 2194 | Created by Priyank Singh | Aug-21 | English | $9.99 | 4h 23m total length | https://www.udemy.com/course/mastering-databricks-apache-spark-build-etl-data-pipeline/ | Priyank Singh | An Engineer who loves to build | 4 | 363 | 2194 | Welcome to the course on Mastering Databricks & Apache spark -Build ETL data pipeline Databricks combines the best of data warehouses and data lakes into a lakehouse architecture. In this course we will be learning how to perform various operations in Scala, Python and Spark SQL. This will help every student in building solutions which will create value and mindset to build batch process in any of the language. This course will help in writing same commands in different language and based on your client needs we can adopt and deliver world class solution. We will be building end to end solution in azure databricks. Key Learning Points We will be building our own cluster which will process our data and with one click operation we will load different sources data to Azure SQL and Delta tables After that we will be leveraging databricks notebook to prepare dashboard to answer business questions Based on the needs we will be deploying infrastructure on Azure cloud These scenarios will give student 360 degree exposure on cloud platform and how to step up various resources All activities are performed in Azure Databricks Fundamentals Databricks Delta tables Concept of versions and vacuum on delta tables Apache Spark SQL Filtering Dataframe Renaming, drop, Select, Cast Aggregation operations SUM, AVERAGE, MAX, MIN Rank, Row Number, Dense Rank Building dashboards Analytics This course is suitable for Data engineers, BI architect, Data Analyst, ETL developer, BI Manager | https://www.udemy.com/course/mastering-databricks-apache-spark-build-etl-data-pipeline/#instructor-1 | Technically sophisticated and business savvy who can play with data having 10+ years of experience and documented history of bridging technical and management acumen in turning around of information technology. Priyank has spent more than 10 years in consulting for building data related processes. Worked with various banking, logistic and insurance companies to process heterogenous amount of big data. Continuously bridging the gaps and deriving values from building data process | Spark | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Science Real-World Case Studies - Hands On Python | Build a Portfolio of Data Science Projects with Pandas ,numpy ,Plotly,Folium,TextBlob,Geopy ,Sklearn & many more .. | 4.5 | 363 | 52231 | Created by Shan Singh | Nov-22 | English | $9.99 | 10h 50m total length | https://www.udemy.com/course/data-science-real-world-use-cases-hands-on-python/ | Shan Singh | Top Rated & Best-Selling Udemy Instructor , Data Scientist | 4.4 | 5625 | 284385 | Are you looking to land a top-paying job in Data Science? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence? If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Data Science is widely adopted in many sectors nowadays such as banking, healthcare, Airlines, Logistic and technology. The purpose of this course is to provide you with knowledge of key aspects of data science applications in business in a practical, easy and fun way. The course provides students with practical hands-on experience using real-world datasets. 1.Task #1 @Predict Success of a Zomato Restaurant : Develop an AI model to predict whether Restaurant can be success or not.. 2.Task #2 @Predict whether News is Fake or not: Develop a Machine Learning Model that can predict whether particular news is fake or not by applying several NLP (Natural Language Processing) Techniques.. 3.Task #3 @Predict sales of a Product: Develop time series forecasting models to predict sales of a product.. Why should you take this Course? It explains Projects on real Data and real-world Problems. No toy data! This is the simplest & best way to become a Data Scientist/AI Engineer/ ML Engineer It shows and explains the full real-world Data. Starting with Understanding Life-Cycle of Project , importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Machine Learning , NLP & Time Series and Data Presentation. It gives you plenty of opportunities to practice and code on your own. Learning by doing. In real-world projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion Guaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee. | https://www.udemy.com/course/data-science-real-world-use-cases-hands-on-python/#instructor-1 | great courses for beginners/working Professionals If anyone has questions about which course may work best for them, please feel free to contact or message me. I will teach you the real-world skills necessary to stand out from the crowd. Whether it’s a Data Science , Data Analysis ,Machine Learning , Time Series or Natural Language Processing skills and more here. Query resolution 24*7 One-on-one support from experts that truly want to help you Query resolution (QnA) - Within 2-3 hours in day time Hardly it can be 8-10 hours.. learn by doing Step-by-step tutorials and project-based learning. more about Shan Professionally, I am a Data Scientist having experience of 7 years in finance, E-commerce, retail and transport. From my courses you will straight away notice how I combine my own experience to deliver content in a easiest fashion. To sum up, I am absolutely passionate about Data Analytics and I am looking forward to sharing my own knowledge with you! | Python | Data Scientist | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Practical Data Science: Analyzing Stock Market Data with R | Learn basic financial technical analysis technics using R (quantmod, TTR) to better understand your favorites stocks. | 4.4 | 360 | 2368 | Created by Manuel Amunategui | Jun-17 | English | $49.99 | 4h 3m total length | https://www.udemy.com/course/practical-data-science-analyzing-stock-market-data-with-r/ | Manuel Amunategui | Data Scientist & Quantitative Developer | 4.5 | 1487 | 47993 | In this class, we will explore various technical and quantitative analysis techniques using the R programming language. I will code as I go and explain what I am doing. All the code is included in PDFs attached to each lecture. I encourage you to code along to not only better understand the concepts but realize how easy they are. What We'll Cover Easily access free, stock-market data using R and the quantmod package Build great looking stock charts with quantmod Use R to manipulate time-series data Create a moving average from scratch Access technical indicators with the TTR package Create a simple trading systems by shifting time series using the binhf package A look at trend-following trading systems using moving averages A look at counter-trend trading systems using moving averages Using more sophisticated indicators (ROC, RSI, CCI, VWAP, Chaikin Volatility) Grouping stocks by theme to better understand them Finding coupling and decoupling stocks within an index What This Class Isn't This class isn't about telling you how to trade or revealing secret trading methods, but to show how easy it is to explore the stock market using R so you can come up with your own ideas. | https://www.udemy.com/course/practical-data-science-analyzing-stock-market-data-with-r/#instructor-1 | Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, author of Monetizing Machine Learning and The Little Book of Fundamental Indicators, founder of FastML, reached top 1% on Kaggle and awarded "Competitions Expert" title, taught over 20,000 students on Udemy and VP of Data Science at SpringML. From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say: "It just ain’t real 'til it reaches your customer’s plate" I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied to machine learning. Reach me at [email protected] | Misc | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Apache Spark 2.0 + Python : DO Big Data Analytics & ML | Project Based, Hands-on Practices, Spark SQL, Spark Streaming, Real life Full cycle Project | 4.3 | 359 | 2374 | Created by V2 Maestros, LLC | Jan-17 | English | $10.99 | 7h 17m total length | https://www.udemy.com/course/apache-spark-20-python-do-big-data-analytics-ml/ | V2 Maestros, LLC | Big Data / Data Science Experts | 50K+ students | 4.2 | 4162 | 76176 | Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This course teaches you Apache Spark 2.0 with Python, trains you in building Spark Analytics and machine learning programs and helps you practice hands-on with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable. Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages. The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you Learn solid fundamentals of the domainSee demos, train and execute solid examplesPractice hands-on and validate it with solutions providedApply knowledge you acquired in an end-to-end real life project Taught by an expert in the field, you will also get prompt response to your queries and excellent support from Udemy. | https://www.udemy.com/course/apache-spark-20-python-do-big-data-analytics-ml/#instructor-1 | V2 Maestros is dedicated to teaching big data / data science courses to students all over the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building Big Data and Data Science talent across the world. | Data Analyst | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Reinforcement Learning beginner to master - AI in Python | Build Artificial Intelligence (AI) agents using Deep Reinforcement Learning and PyTorch: A2C, REINFORCE, DQN, etc. | 4.3 | 357 | 2834 | Created by Escape Velocity Labs | Jul-22 | English | $9.99 | 10h 46m total length | https://www.udemy.com/course/beginner-master-rl-1/ | Escape Velocity Labs | Hands-on, comprehensive AI courses | 4.5 | 616 | 5359 | This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with Deep Learning techniques and neural networks, giving rise to the branch known as Deep Reinforcement Learning. This course will give you the foundation you need to be able to understand new algorithms as they emerge. It will also prepare you for the next courses in this series, in which we will go much deeper into different branches of Reinforcement Learning and look at some of the more advanced algorithms that exist. The course is focused on developing practical skills. Therefore, after learning the most important concepts of each family of methods, we will implement one or more of their algorithms in jupyter notebooks, from scratch. This course is divided into three parts and covers the following topics: Part 1 (Tabular methods): - Markov decision process - Dynamic programming - Monte Carlo methods - Time difference methods (SARSA, Q-Learning) - N-step bootstrapping Part 2 (Continuous state spaces): - State aggregation - Tile Coding Part 3 (Deep Reinforcement Learning): - Deep SARSA - Deep Q-Learning - REINFORCE - Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method) | https://www.udemy.com/course/beginner-master-rl-1/#instructor-1 | Escape Velocity Labs offers courses in Artificial Intelligence and data science. The courses are designed to develop the student's practical skills and acquire the necessary knowledge to work in the field. The theory is taught in a simple but rigorous manner. The company was founded in 2019 and in addition to creating online training courses it offers consulting services in the area of Machine Learning to companies. | Python | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Advanced Data Analytics using Python [2022] | Master Advanced Data Analytics by solving Real-Life Analytics Problems using Python. Learn by doing! | 4.7 | 356 | 28239 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 17h 49m total length | https://www.udemy.com/course/data-analytics-python/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Welcome to the online course on Advanced Data Analytics using Python. Data analysts exist at the intersection of information technology, statistics and business. They combine these fields in order to help businesses and organizations succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data. In this course, you will get advanced knowledge on Data Analytics. This course begins with providing you the complete knowledge on Python programming language. You will learn all the concepts of python programming from basics to advanced level. This course will cover the following topics:- Variables and data types Loops and conditionals Functions Object oriented programming Dates and times Regular expressions Numpy and pandas library Along with python programming, this course will cover other data analytics concepts such as Data Visualization Data cleaning Query Analysis Data Exploration Statistics and Probability concepts All these topics are covered in detail. Along with theory you will get to practice them using some real world datasets. There will be lots of exercises and quizzes. Not only this, you will get to work on some exciting projects including Startups Case Study and Analysis, IPL Player performance Analysis. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! Lots of exercises and quizzes are waiting for you. You will also have access to all the resources used in this course. Enroll now and become an expert in data analytics. | https://www.udemy.com/course/data-analytics-python/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Data Analyst | Grandmaster | >=4 | Below 1K | >=25K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Statistics with R - Intermediate Level | Statistical analyses using the R program | 4.5 | 354 | 31502 | Created by Bogdan Anastasiei | Dec-20 | English | $9.99 | 2h 24m total length | https://www.udemy.com/course/statistics-with-r-intermediate-level/ | Bogdan Anastasiei | University Teacher and Consultant | 4.5 | 7750 | 296607 | If you want to learn how to perform the most useful statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to do a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to perform a sequential regression analysis or how to compute the Cronbach’s alpha. Everything is here, in this course, explained visually, step by step. So, what will you learn in this course? First of all, you will learn how to perform association tests in R, both parametric and non-parametric: the Pearson correlation, the Spearman and Kendall correlation, the partial correlation and the chi-square test for independence. The test of mean differences represent a vast part of this course, because of their great importance. We will approach the t tests, the analysis of variance (both univariate and multivariate) and a few non-parametric tests. For each technique we will present the preliminary assumption, run the procedure and carefully interpret all the results. Next you will learn how to perform a multiple linear regression analysis. We have assign several big lectures to this topic, because we will also learn how to check the regression assumptions and how to run a sequential (or hierarchical) regression in R. Finally, we will enter the territory of statistical reliability – you will learn how to compute three important reliability indicators in R. So after graduating this course, you will get some priceless statistical analysis knowledge and skills using the R program. Don’t wait, enroll today and get ready for an exciting journey! | https://www.udemy.com/course/statistics-with-r-intermediate-level/#instructor-1 | My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting. | Statistics | Consultant | Yes | >=4 | Below 1K | >=30K | >=4 | Below 10 K | >=2.5 Lakh | ||||||||||||||||
What is Data Science? | Build your mathematics and statistics foundations strongly and ensure your Data science fundas are in place! Q4 Updated | 3.6 | 352 | 36318 | Created by Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! | Jul-20 | English | $9.99 | 4h 31m total length | https://www.udemy.com/course/what-is-data-science-r/ | Sai Acuity Institute of Learning Pvt Ltd Enabling Learning Through Insight! | Cybersecurity, Data Science & Human Capital Practitioners! | 3.9 | 6592 | 315263 | A Data Scientist dons many hats in his/her workplace. Not only are Data Scientists responsible for business analytics, they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms Data Analytics career prospects depend not only on how good are you with programming —equally important is the ability to influence companies to take action. As you work for an organization, you will improve your communication skills. A Data Analyst interprets data and turns it into information which can offer ways to improve a business, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends – as such a Data Analyst job description should highlight the analytical nature of the role. Key skills for a data analyst A high level of mathematical ability. Programming languages, such as SQL, Oracle, and Python. The ability to analyze, model, and interpret data. Problem-solving skills. A methodical and logical approach. The ability to plan work and meet deadlines. Accuracy and attention to detail. What will I learn in this course: You will gain a firm foothold on the fundamentals of Data Science. You will understand the important terminologies and statistical methods in data science You will understand the mathematics and statistics behind Machine Learning You will learn how to pre-process data You will learn what percentile is with the help of examples You will learn the fundamental concepts of descriptive statistics You will learn how to collect data, how to visualize data, how to predict or explain different behaviors and events and how to find ideas for data research. | https://www.udemy.com/course/what-is-data-science-r/#instructor-1 | We specialize in Cybersecurity, Data Science and Talent Management/Human capital management training. The USP of all our training's is the hands-on that we provide, our focus is on real-life practical knowledge sharing, and not tool-based PPT slides. All our training's are conducted by highly experienced practitioners who are dyed-in-the-wool penetration testers. The material is cutting edge and updated with even the most recent developments. We have a standard set of courses outlined in different information security domains, data analytics domains and Talent management domain. However, we also customize the training according to the clients’ requirements. | Misc | >=3 | Below 1K | >=35K | >=3 | Below 10 K | >=3 Lakh | ||||||||||||||||||
Learn Machine Learning & Data Mining with Python | Learn Building Machine Learning & Deep Learning Models in Python, and use the Results in Data Mining Analyses | 4.6 | 352 | 1105 | Created by Data Science Guide | May-22 | English | $10.99 | 8h 37m total length | https://www.udemy.com/course/implement-machine-learning-in-data-mining-using-python/ | Data Science Guide | Data Scientist & SQL Developer | 4.3 | 2230 | 5742 | If you seek to learn how to create machine learning models and use them in data mining process, this course is for you. You will understand in this course what is data mining process and how to implement machine learning algorithms in data mining. Moreover, you will learn in details how deep learning does work and how to build a deep learning model to solve a business problem. In the beginning of the course, you will understand the basic concepts of data mining and learn about the business fields where data mining is implemented. After that you will learn how to create machine learning models in Python using several data science libraries developed especially for this purpose. NumPy, Pandas, and Matplotlib are some examples of these models that you will learn how to import and use to create machine learning algorithms in Python. You will learn typing codes in Python from scratch without the need to have a pervious knowledge in coding. You will be familiar with the essential code needed to build machine learning models. This course is designed to provide you with the knowledge you need in a simple and straightforward way to smooth the learning process. You will build your knowledge step by step until you become familiar with the most used Machine Learning algorithms. | https://www.udemy.com/course/implement-machine-learning-in-data-mining-using-python/#instructor-1 | My name is Abraham Joudah I have worked in IT and Data Science for more than 15 years. After completed my bachelor’s in computer science, I worked as Database Administrator in one of the engineering companies. I have obtained several certificates from Microsoft like MCSE, MCDBA and MCSA. After several years of working in IT, I started focusing on Data Science field and learning SQL in depth to enhance my business data analysis skills. I also worked as Data Analyst in several companies. Over several years of working in this field I mastered several analytical tools, such as: R, SAS, SQL, Tableau, and Excel. As I enjoy working at data science field I pursued my study in this major and obtained my Master’s degree in Business Analytics from the University of North Texas. I love teaching Data Science, So I decided to create several courses in this field to share my knowledge with others. I tried to be more practical in my classes rather than repeating the same materials and curriculums in this field. I used materials based on real business scenarios to provide practical knowledge for students. I focused on examples from real business. In other words, I created shortcuts for learners to gain practical experience while studying my courses. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
Data Science:Data Mining & Natural Language Processing in R | Harness the Power of Machine Learning in R for Data/Text Mining, & Natural Language Processing with Practical Examples | 4.3 | 352 | 3809 | Created by Minerva Singh | Oct-22 | English | $11.99 | 13h 15m total length | https://www.udemy.com/course/data-science-datamining-natural-language-processing-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniquesData Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etcCreating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MOREStatistical analysis, statistical inference, and the relationships between variables.Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and RegressionWeb-Scraping using RExtracting text data from Twitter and Facebook using APIsText miningCommon Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! | https://www.udemy.com/course/data-science-datamining-natural-language-processing-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | NLP | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Forecast Anything with Excel | Learn to create your own FORECASTS with just Microsoft Excel: trends, seasonalities, forecast intervals and more! | 3.6 | 350 | 1912 | Created by Mauricio Maroto | Aug-17 | English | $9.99 | 3h 45m total length | https://www.udemy.com/course/forecast-anything-with-excel/ | Mauricio Maroto | +10,000 students and growing! | 4.2 | 1547 | 11603 | +1,600 Worldwide Students enrolled ═════════════════════════════════════════════════════ Some Student Reviews are: "Professional and very useful | https://www.udemy.com/course/forecast-anything-with-excel/#instructor-1 | Mauricio Maroto holds a Master degree in Industrial Economics from Carlos III University (Madrid, Spain). With more than 8 years of professional experience, he's always been involved in creating insights for strategic decision making through data analysis. From data query, cleaning and processing, to clustering, regression and classification techniques, Mauricio is passionate about finding patterns and trends in large datasets. Mauricio's other passions are his family, friends, his pets and he loves to play soccer, whether indoors or outdoors. He also goes out for a run on weekends (not lately). He also loves topics such as Economics, Programming and Science. | Excel | >=3 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Applied Text Mining and Sentiment Analysis with Python | Perform Sentiment Analysis on Twitter data by combining Text Mining and NLP techniques, NLTK and Scikit-Learn | Bestseller | 4.4 | 348 | 4374 | Created by Benjamin Termonia | Nov-21 | English | $9.99 | 2h 18m total length | https://www.udemy.com/course/applied-text-mining-and-sentiment-analysis-with-python/ | Benjamin Termonia | Artificial Intelligence & Automation Passionate | 4.4 | 612 | 10649 | "Bitcoin (BTC) price just reached a new ALL TIME HIGH! #cryptocurrency #bitcoin #bullish" For you and me, it seems pretty obvious that this is good news about Bitcoin, isn't it? But is it that easy for a machine to understand it? ... Probably not ... Well, this is exactly what this course is about: learning how to build a Machine Learning model capable of reading and classifying all this news for us! Since 2006, Twitter has been a continuously growing source of information, keeping us informed about all and nothing. It is estimated that more than 6,000 tweets are exchanged on the platform every second, making it an inexhaustible mine of information that it would be a shame not to use. Fortunately, there are different ways to process tweets in an automated way, and retrieve precise information in an instant ... Interested in learning such a solution in a quick and easy way? Take a look below ... _____________________________________________________ What will you learn in this course? By taking this course, you will learn all the steps necessary to build your own Tweet Sentiment prediction model. That said, you will learn much more as the course is separated into 4 different parts, linked together, but providing its share of knowledge in a particular field (Text Mining, NLP and Machine Learning). SECTION 1: Introduction to Text Mining In this first section, we will go through several general elements setting up the starting problem and the different challenges to overcome with text data. This is also the section in which we will discover our Twitter dataset, using libraries such as Pandas or Matplotlib. SECTION 2: Text Normalization Twitter data are known to be very messy. This section will aim to clean up all our tweets in depth, using Text Mining techniques and some suitable libraries like NLTK. Tokenization, stemming or lemmatization will have no secret for you once you are done with this section. SECTION 3: Text Representation Before our cleansed data can be fed to our model, we will need to learn how to represent it the right way. This section will aim to cover different methods specific to this purpose and often used in NLP (Bag-of-Words, TF-IDF, etc.). This will give us an additional opportunity to use NLTK. SECTION 4: ML Modelling Finally ... the most exciting step of all! This section will be about putting together all that we have learned, in order to build our Sentiment prediction model. Above all, it will be about having an opportunity to use one of the most used libraries in Machine Learning: Scikit-Learn (SKLEARN). _____________________________________________________ Why is this course different from the others I can find on the same subject? One of the key differentiators of this course is that it's not about learning Text Mining, NLP or Machine Learning in general. The objective is to pursue a very precise goal (Sentiment Analysis) and deepen all the necessary steps in order to reach this goal, by using the appropriate tools. So no, you might not yet be an unbeatable expert in Artificial Intelligence at the end of this course, sorry ... but you will know exactly how, and why, your Sentiment application works so well. _____________________________________________________ About AIOutsider AIOutsider was created in 2020 with the ambition of facilitating the learning of Artificial Intelligence. Too often, the field has been seen as very opaque or requiring advanced knowledge in order to be used. At AIOutsider, we want to show that this is not the case. And while there are more difficult topics to cover, there are also topics that everyone can reach, just like the one presented in this course. If you want more, don't hesitate to visit our website! _____________________________________________________ So, if you are interested in learning AI and how it can be used in real life to solve practical issues like Sentiment Analysis, there is only one thing left for you to do ... learn with us and join this course! | https://www.udemy.com/course/applied-text-mining-and-sentiment-analysis-with-python/#instructor-1 | Hi! My name is Benjamin and I am passionate about Data. I have been working in the Financial Industry for more than 6 years as a Data Analyst. From M&A and Wealth Management to Commodity Trading, and from SME to MNC, I noticed an essential common point ... Data. Data is what makes a lot of businesses strong and able to cope with market requirements, and this trend is not about to stop! That's why knowing how to provide a company with accurate and timely analysis is more than ever a way to stand out from its peers. My objective as instructor is to show that Automation and Artificial Intelligence is within everyone's reach, and not as complicated as it might seem. | Python | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Generate and visualize data in Python and MATLAB | Learn how to simulate and visualize data for data science, statistics, and machine learning in MATLAB and Python | 4.4 | 346 | 19949 | Created by Mike X Cohen | Oct-22 | English | $9.99 | 6h 24m total length | https://www.udemy.com/course/suv-data-mxc/ | Mike X Cohen | Neuroscientist, writer, professor | 4.6 | 36367 | 188013 | Data science is quickly becoming one of the most important skills in industry, academia, marketing, and science. Most data-science courses teach analysis methods, but there are many methods; which method do you use for which data? The answer to that question comes from understanding data. That is the focus of this course. What you will learn in this course: You will learn how to generate data from the most commonly used data categories for statistics, machine learning, classification, and clustering, using models, equations, and parameters. This includes distributions, time series, images, clusters, and more. You will also learn how to visualize data in 1D, 2D, and 3D. All videos come with MATLAB and Python code for you to learn from and adapt! This course is for you if you are an aspiring or established: Data scientist Statistician Computer scientist (MATLAB and/or Python) Signal processor or image processor Biologist Engineer Student Curious independent learner! What you get in this course: >6 hours of video lectures that include explanations, pictures, and diagrams pdf readers with important notes and explanations Exercises and their solutions MATLAB code and Python code With >4000 lines of MATLAB and Python code, this course is also a great way to improve your programming skills, particularly in the context of data analysis, statistics, and machine learning. What do you need to know before taking this course? You need some experience with either Python or MATLAB programming. You don't need to be an expert coder, but if you are comfortable working with variables, for-loops, and basic plotting, then you already know enough to take this course! | https://www.udemy.com/course/suv-data-mxc/#instructor-1 | I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations. But you're here because of my teaching, so let me tell you about that: I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way. I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style. Over 120,000 students have watched over 7,500,000 minutes of my courses. Come find out why! I have several free courses that you can enroll in. Try them out! You got nothing to lose 😉 ------------------------- By popular request, here are suggested course progressions for various educational goals: MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python Applied linear algebra: Complete Linear Algebra; Dimension Reduction Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing | Python | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=15K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Causal Data Science with Directed Acyclic Graphs | Get to know the modern tools for causal inference from machine learning and AI, with many practical examples in R | 4.5 | 345 | 1962 | Created by Paul Hünermund | Sep-20 | English | $9.99 | 4h 57m total length | https://www.udemy.com/course/causal-data-science/ | Paul Hünermund | Professor for Business Economics | 4.5 | 345 | 1962 | This course offers an introduction into causal data science with directed acyclic graphs (DAG). DAGs combine mathematical graph theory with statistical probability concepts and provide a powerful approach to causal reasoning. Originally developed in the computer science and artificial intelligence field, they recently gained more and more traction also in other scientific disciplines (such as, e.g., machine learning, economics, finance, health sciences, and philosophy). DAGs allow to check the validity of causal statements based on intuitive graphical criteria, that do not require any algebra. In addition, they open up the possibility to completely automatize the causal inference task with the help of special identification algorithms. As an encompassing framework for causal thinking, DAGs are becoming an essential tool for everyone interested in data science and machine learning. The course provides a good overview of the theoretical advances that have been made in causal data science during the last thirty year. The focus lies on practical applications of the theory and students will be put into the position to apply causal data science methods in their own work. Hands-on examples, discussed in the statistical software package R, will guide through the presented material. There are no particular prerequisites for participating. However, a good working knowledge in probability and basic programming skills are a benefit. | https://www.udemy.com/course/causal-data-science/#instructor-1 | Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School. In his research, Dr. Hünermund studies how firms can leverage new technologies in the space of machine learning and artificial intelligence for value creation and competitive advantage. His work explores the potential for biases in organizational decision-making and ways for managers to counter them. It thereby sheds light on the origins of effective business strategies in markets characterized by a high degree of technological competition and the resulting implications for economic growth and environmental sustainability. To study the determinants of firm innovation activities and performance, his research builds on ideas from a range of disciplines including economics, business strategy, game theory, and psychology. Furthermore, it employs a variety of methods from econometrics, machine learning, and the field of causal inference. Dr. Hünermund’s work provides insights for policymakers on how to optimally designing public R&D support schemes, which he has communicated widely in consulting projects and keynote addresses to the European Commission, the German Federal Ministry of Research and Education, and the OECD. He is the co-founder of causalscience[dot]org, a platform for fostering knowledge exchange between industry and academia on topics related to causal data science. His research has been published in Journal of Management Studies, Research Policy, Journal of Product Innovation Management, International Journal of Industrial Organization, and Harvard Business Review, among others. Dr. Hünermund serves on the editorial board of the Journal of Causal Inference and on the executive team of the Technology and Innovation Management division at the Academy of Management. He studied economics at the University of Mannheim, HEC Lausanne, and NYU Stern School of Business, and earned a Ph.D. in business economics at KU Leuven in Belgium. His work has been covered by Frankfurter Allgemeine Zeitung, Süddeutsche Zeitung and Neue Zürcher Zeitung. In 2021, Capital Magazine voted him on its “top 40 under 40” list. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Science: Transformers for Natural Language Processing | BERT, GPT, Deep Learning, Machine Learning, & NLP with Hugging Face, Attention in Python, Tensorflow, PyTorch, & Keras | Bestseller | 4.6 | 343 | 1667 | Created by Lazy Programmer Team, Lazy Programmer Inc. | Nov-22 | English | $79.99 | 17h 53m total length | https://www.udemy.com/course/data-science-transformers-nlp/ | Lazy Programmer Team | Artificial Intelligence and Machine Learning Engineer | 4.7 | 47728 | 178359 | Hello friends! Welcome to Data Science: Transformers for Natural Language Processing. Ever since Transformers arrived on the scene, deep learning hasn't been the same. Machine learning is able to generate text essentially indistinguishable from that created by humans We've reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and more We've created multi-modal (text and image) models that can generate amazing art using only a text prompt We've solved a longstanding problem in molecular biology known as "protein structure prediction" In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work. This is different from most other resources, which only cover the former. The course is split into 3 major parts: Using Transformers Fine-Tuning Transformers Transformers In-Depth PART 1: Using Transformers In this section, you will learn how to use transformers which were trained for you. This costs millions of dollars to do, so it's not something you want to try by yourself! We'll see how these prebuilt models can already be used for a wide array of tasks, including: text classification (e.g. spam detection, sentiment analysis, document categorization) named entity recognition text summarization machine translation question-answering generating (believable) text masked language modeling (article spinning) zero-shot classification This is already very practical. If you need to do sentiment analysis, document categorization, entity recognition, translation, summarization, etc. on documents at your workplace or for your clients - you already have the most powerful state-of-the-art models at your fingertips with very few lines of code. One of the most amazing applications is "zero-shot classification", where you will observe that a pretrained model can categorize your documents, even without any training at all. PART 2: Fine-Tuning Transformers In this section, you will learn how to improve the performance of transformers on your own custom datasets. By using "transfer learning", you can leverage the millions of dollars of training that have already gone into making transformers work very well. You'll see that you can fine-tune a transformer with relatively little work (and little cost). We'll cover how to fine-tune transformers for the most practical tasks in the real-world, like text classification (sentiment analysis, spam detection), entity recognition, and machine translation. PART 3: Transformers In-Depth In this section, you will learn how transformers really work. The previous sections are nice, but a little too nice. Libraries are OK for people who just want to get the job done, but they don't work if you want to do anything new or interesting. Let's be clear: this is very practical. How practical, you might ask? Well, this is where the big bucks are. Those who have a deep understanding of these models and can do things no one has ever done before are in a position to command higher salaries and prestigious titles. Machine learning is a competitive field, and a deep understanding of how things work can be the edge you need to come out on top. We'll also look at how to implement transformers from scratch. As the great Richard Feynman once said, "what I cannot create, I do not understand". SUGGESTED PREREQUISITES: Decent Python coding skills Deep learning with CNNs and RNNs useful but not required Deep learning with Seq2Seq models useful but not required For the in-depth section: understanding the theory behind CNNs, RNNs, and seq2seq is very useful UNIQUE FEATURES Every line of code explained in detail - email me any time if you disagree No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch Not afraid of university-level math - get important details about algorithms that other courses leave out Thank you for reading and I hope to see you soon! | https://www.udemy.com/course/data-science-transformers-nlp/#instructor-1 | Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. | NLP | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||
Machine Learning for Data Science using MATLAB | Learn to implement classification and clustering algorithms using MATLAB with practical examples, projects and datasets | 4.5 | 341 | 1758 | Created by Nouman Azam | Oct-21 | English | $11.99 | 15h 56m total length | https://www.udemy.com/course/machine-learning-for-datascience-using-matlab/ | Nouman Azam | Your MATLAB Professor | 4.4 | 4786 | 37337 | Basic Course Description This course is for you if you want to have a real feel of the Machine Learning techniques without having to learn all the complicated maths. Additionally, this course is also for you if you have had previous hours and hours of machine learning theory but could never got a change or figure out how to implement and solve data science problems with it. The approach in this course is very practical and we will start everything from very scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineer and science students and is frequently used by top data science research groups world wide. Below is the brief outline of this course. Segment 1: Introduction to course In this section we spend some time talking about the topics you’ll learn, the approach of learning used in the course, essential details about MATLAB to get you started. This will give you an idea of what to expect from the course. Segment 2: Data preprocessing (Brief videos) We need to prepare and preprocess our data before applying Data Science algorithms and techniques. This section discusses the essential preprocessing techniques and discuses the topics such as getting rid of outliers, dealing with missing values, converting categorical data to numerical form, and feature scalling. Segment 3: Classification Algorithms in MATLAB Classification algorithms is an important class of Data Science algorithms and is a must learn for every data scientist. This section provides not only the intuition behind some of the most commonly used classification algorithm but also provides there implementation in MATLAB. The algorithms that we cover are K-Nearest Neighbor Naïve Bayesain Support Vector Machine Decision Trees Discriminant Analysis Ensembles In addition to these we also cover how to evaluate the performance of classifiers using different metrics. Segment 4: Clustering Algorithms in MATLAB This section introduces some of the commonly used clustering algorithms alongside with their intuition and implementation in MATLAB. We also cover the limitations of clustering algorithms by looking at their performance when the clusters are of different sizes, shapes and densities. The algorithms we cover in this section are K-Means Mean Shift DBSCAN Hierarchical Clustering In the same section, we also cover practical application of the clustering algorithms by looking at the applications of image compression and sentence grouping. This section provides some intuition regarding the strengths of clustering in real life data analysis tasks. Segment 5: Dimensionality Reduction Dimensionality reduction is an important branch of algorithms in Data Science. In this section we show how to reduce the dimensions for a specific Data Science problems so that the visualization becomes easy. We cover the PCA algorithm in this section. Segment 6: Project: Malware Analysis In this section we provide a detailed project on malware analysis from one of our recent research paper. We provide introductory videos on how to complete the project. This will provide you with some hands on experience for analyzing Data Science problems. Segment 7: Data preprocessing (Detailed Videos) In this section we dive deep into the topic of data preprocessing and cover many interesting topics. The topic in this section include Dealing with missing data using Deleting strategies Using mean and mode Radom values for handling missing data Class based strategies Considering as a special value Dealing with Categorical Variables using the One hot encoding Frequency based encoding Target based encoding Encoding in the presence of an order Outlier Detection using 3 sigma rule with Box plot rule Histogram based rule Local outlier factor Outliers in categorical variable Feature Scaling and Data Discretization ___________________________________________________________________________ Your Benefits and Advantages: If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked! You will be sure of receiving quality contents since the instructors has already many courses in the MATLAB on udemy. You have lifetime access to the course. You have instant and free access to any updates i add to the course. You have access to all Questions and discussions initiated by other students. You will receive my support regarding any issues related to the course. Check out the curriculum and Freely available lectures for a quick insight. ___________________________________________________________________________ It's time to take Action! Click the "Take This Course" button at the top right now! ...Time is limited and Every second of every day is valuable... We are excited to see you in the course! Best Regrads, Dr. Nouman Azam _______________________________________________ More Benefits and Advantages: ✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses on Udemy in the MATLAB niche. ✔ The titles of these courses are Complete MATLAB Tutorial: Go from Beginner to Pro MATLAB App Desigining: The Ultimate Guide for MATLAB Apps Go From Zero to Expert in Building Regular Expressions Master Cluster Analys for Data Science using Python Learn MATLAB Programming Skills while Solving Problems _______________________________________________ Student Testimonials for Dr. Nouman Azam! ★★★★★ This is the second Udemy class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took. Jeff Philips ★★★★★ Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up! Oamar Kanji ★★★★★ The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you! Josh Nicassio Student Testimonials! who are also instructors in the MATLAB category ★★★★★ "Concepts are explained very well, Keep it up Sir...!!!" Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)" | https://www.udemy.com/course/machine-learning-for-datascience-using-matlab/#instructor-1 | I am Dr. Nouman Azam and i am Associate Professor in Computer Science. I teach online courses related to MATLAB Programming and i have a rich community of students comprising of more than 25,000 students on different online plateforms. The focus in these courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In my courses, you will find topics such as MATLAB programming, designing gui's, data analysis and visualization. Machine learning techinques using MATLAB is one of my favourate topic. During my research career i explore the use of MATLAB in implementing machine learning techniques such as bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems and medical decision making. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Linear Regression and Logistic Regression in Python | Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners | 4.7 | 340 | 56201 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 7h 33m total length | https://www.udemy.com/course/linear-regression-and-logistic-regression-in-python-starttech/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in Python, right? You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning. Create a linear regression and logistic regression model in Python and analyze its result. Confidently model and solve regression and classification problems A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation Section 2 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data Preprocessing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 5 - Regression Model This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. How this course will help you? If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular techniques of machine learning, which is Linear Regression and Logistic Regregression Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear and logistic regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of Linear and Logistic Regression modelling - Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. | https://www.udemy.com/course/linear-regression-and-logistic-regression-in-python-starttech/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Python | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS | Data Science Python-Learn Statistics for Data Science, Machine Learning for Data Science, Deep Learning for Data Science | 4.3 | 340 | 3410 | Created by AI Sciences, AI Sciences Team | Nov-22 | English | $9.99 | 93h 56m total length | https://www.udemy.com/course/data-science-machine-learningtheoryprojectsa-z-90-hours/ | AI Sciences | AI Experts & Data Scientists |4+ Rated | 160+ Countries | 4.5 | 2414 | 40750 | Comprehensive Course Description: Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. Fast forward to today. It’s the same story with Artificial Intelligence (AI). The field of AI was formally founded in 1956. But it’s only now—more than six decades later—that AI is expected to revolutionize the way humanity will live and work in the coming decades. Data science is a large field of study that covers data systems and processes. These systems and processes are aimed at maintaining data sets as well as getting meaning out of them. Machine Learning (ML), a branch of AI, is the concept that systems can automatically learn and adapt from experience without human intervention. ML, essentially, aims to equip machines with independent learning techniques. Data Science & Machine Learning Full Course in 90 Hours is exhaustive and covers various topics in both these fields in great detail. Data science specialists use a combination of algorithms, applications, principles, and tools to gain a real sense of random data clusters. You are probably aware that organizations worldwide are generating exponential amounts of data. So, monitoring and storing all this data becomes very difficult. This is where data science plays a vital role by focusing on data modeling and data warehousing. Both AI and ML are important to data scientists because they can work equally well in both with ease. The expertise of these skilled professionals allows them to switch roles quickly, too. And in the life cycle of a data science project, this can be a critical factor. What makes this Data Science and Machine Learning course unique? This learning by doing course provides you with not only a solid theoretical foundation but also practical hands-on training in data science and machine learning. At the end of this course, you will be equipped with the knowledge of all the essential concepts you need to excel as a Data Science professional. When you take a quick look at the different sections of this all-inclusive course, you may think of these sections as being independent. But that’s not the case. These sections are interlinked and almost sequential. While it’s true that the course is divided into multiple sections, it’s also true that each section is an independent concept, or you can view it as a course on its own. We have deliberately arranged these sections in a sequence. The reason for this is each subsequent section builds upon the sections you have completed. This framework enables you to explore more independent concepts easily. Data Science & Machine Learning Full Course in 90 HOURS is crafted to teach you the most in-demand skills in the real world. This course aims to help you understand all the data science and machine learning concepts and methodologies with regards to Python. The course is: · Comfortably paced. · Easy to understand. · Descriptive and expressive. · Exhaustive. · Practical with live coding. · Rich with the most advanced and recently discovered models and breakthroughs by the champions in the AI universe. This course is designed for beginners, but we will explore complex concepts gradually. You will find this course interesting, and you will move ahead easily, as it is a compilation of all the basics. You will make quick progress and experience more than what you have learned. At the end of every subsection, you are assigned Home Work/exercises/activities to assess / further strengthen your learning. All this assessment is based on the previous concepts and methods you have learned. Several of these assessment tasks will be coding based, as the main aim is to get you up and proceed to implementations. Data Science is doubtless a rewarding career. You get to resolve some of the most interesting data issues and earn a handsome salary package for your efforts. After you finish Data Science & Machine Learning Full Course in 90 HOURS, you will be able to easily tackle real-world problems and ensure steady career growth. Unlike other courses, this comprehensive course is not expensive. In fact, you can learn all the concepts and methodologies of Data Science and Machine Learning at a fraction of the cost. Our tutorials are divided into 700+ brief HD videos along with detailed code notebooks. Enroll in this course and start your learning journey in Data Science and Machine Learning. This course really simplifies all the complex concepts for you. You will not find an easier course that inspires you as much along your learning journey. Teaching is our passion: We work meticulously to create online tutorials with instructors who are willing to share their expertise and help you in understanding all the concepts. The aim is to create a strong basic understanding for you before you move onward to the advanced version. Detailed course notes, high-quality video content, learning assessment questions, meaningful course material, and subject-related handouts are some of the perks of this course. You are also assured of the support of a dedicated instructor every step of the way. You can approach our team in case of any queries. Course content: 1. Python for Data Science and Data Analysis a. You start with problem-solving and finish with fancy indexing and plots in Matplotlib. b. No prior knowledge in any computer science language is assumed. c. Great fun with Python language. d. Reasonable treatment of data science packages (NumPy, Pandas, Matplotlib, Seaborn, and Sklearn). e. After this course, you will be a competent Python programmer as well as a reasonable expert of data science packages (NumPy, Pandas, Matplotlib). f. This section is designed to teach you programming in general also. Therefore, shifting from this language to any other language after this section is not difficult. 2. Data Understanding and Data Visualization with Python a. This section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization. b. While Section 1 focuses more on Python language, this section focuses completely on data science packages and their efficient use. c. The packages covered in this section include NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium. d. As far as we know, this is the most comprehensive section on data understanding and visualization among the available ones. e. Further, this section is designed to reduce the dependency on core Python language to be treated independently, as well. f. 2D and 3D visualizations, interactive visualizations, and geographic maps are also covered. g. Proceeding in data science with being able to effectively play with the data using famous packages makes progress much worse, and this section addresses this concern. 3. Mastering Probability and Statistics in Python a. Obviously, concepts in data science are not new. In fact, it is also believed that data science is merely a renamed version of Probability and Statistics. Well, without being biased to that extent, we will say that the practical nature of applications was uncovered earlier even though the theory traces back to Probability and Statistics. b. One way or the other, knowing Probability and Statistics makes a significant theoretical as well as practical difference. c. Most of the courses on Probability and Statistics, however, fail to link the data science practices and theory by merely focusing on the axiomatic treatment of the subject. d. We build this section by keeping the practical needs of data science in mind as well as the importance of theory. e. Wherever important, we deliberately explain and show the relationships by derivations and even through Python Code. f. This section builds a very sound basis for understanding the classical concepts in data science as well as its more recent generalizations. g. We start with the very basics of Probability, go through inference and estimations, link famous machine learning techniques with conditional probability, and finally, show that Deep Neural Networks indeed learn a probability function eventually. 4. Machine Learning Crash Course a. Although several concepts, or even all, fall under the umbrella of Probability and Statistics, it turns out that most of the concepts have made their own practical place, mostly derived through engineering, with the name of Machine Learning. For example, the term “overfitting” is now referring to the area of machine learning. b. Machine Learning brings its own set of practices to reach the demands of automation. Hence, mastering these concepts becomes inevitable. c. This section is actually a quick walkthrough of the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts. d. We mostly cover applications using the Sklearn Python package and build machine learning pipelines in this section. e. We also elaborate on more advanced areas of machine learning, which we later present as separate sections. 5. Feature Engineering and Dimensionality Reduction with Python a. Knowing the sections you have covered thus far certainly brings you a huge clarity of the field. But there is still one thing that brings the improvements in the results with a reasonable margin, and that is data preprocessing or data preparation. b. Most of the data science today relies on preparing the data suitable for machine learning models. An effective way of data preparation, most of the time, becomes a game-changer. c. This section focuses on data preparation for machine learning models. d. We build this section to provide an understanding of why selecting features and transforming features are important. e. We also discuss practical issues with real data, like missing values and non-numeric data. f. We discuss the performance improvements both in terms of execution time as well as the accuracy of the models. g. We explain the required mathematical background in a simple way. h. Finally, all the concepts are made more easily understandable by coding relevant examples in Python. 6. Artificial Neural Networks with Python a. With the availability of a huge quantity of data as well as computation power, a relatively old machine learning model, Artificial Neural Network turns out to be the game-changer in data science. b. Artificial Neural Network can approximate almost any pattern in the data. Further, it has a much greater data utilization capacity as compared to the more classical methods. c. With the recent rise of ANNs, a lot of practical techniques are also discovered, particularly for ANNs. d. Also, working with a large amount of data brings its own challenges for learning algorithms. e. In this section, we address all these concerns and cover ANNs in depth. f. We also introduce another framework, “TensorFlow,” for working in ANNs. g. With this section in hand, you can now target much larger machine learning problems. 7. Convolutional Neural Networks with Python a. ANNs, in its most basic form, is not that suitable for image data and for the problems in computer vision. b. Convolutional Neural Networks (CNNs) are considered a game-changer in the field of computer vision. CNNs are not limited to images only. You’ll find them everywhere now, from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes inevitable in all the fields of data science. Even most of the Recurrent Neural Networks (RNNs) rely on CNNs nowadays. c. In this section, you will to learn about: i. The significance of CNNs in data science. ii. The reasons to shift to CNNs from hand engineering (classical computer vision). iii. The major concepts from the absolute beginning with complete unfolding with examples in Python. iv. Practical explanation and live coding with Python. v. Evolution of CNNs — LeNet (1990s) to MobileNets (2020s). vi. Intricate details of CNNs including examples of training CNNs. vii. TensorFlow (Google’s deep learning framework). viii. The use and applications of CNNs (with implementations in framework TensorFlow) that are more recent and advanced in terms of accuracy and efficiency. ix. The use and applications of pre-trained CNNs (with implementations in framework TensorFlow) for transfer learning on your own dataset. x. Building your own applications for Human Face-Verification and Neural Style Transfer. After completing this course successfully, you will be able to: · Relate the concepts, principles, and theories in Data Science & Machine Learning. · Understand the methodology of Data Science & Machine Learning using real datasets. Who this course is for: · People who want to become perfect in their data speak. · People who want to learn Data Science & Machine Learning with real datasets in Data Science. · People from a non-engineering background who want to enter the Data Science field. · People who want to enter the Machine Learning field. · Individuals who are passionate about numbers and programming. · People who want to learn Data Science & Machine Learning along with its implementation in realistic projects. · Data Scientists. · Business Analysts. | https://www.udemy.com/course/data-science-machine-learningtheoryprojectsa-z-90-hours/#instructor-1 | We are a group of experts, PhDs and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. We decided to produce a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence, and Data Science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience. Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
TensorFlow 2.0 Practical Advanced | Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects | 3.7 | 336 | 5224 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency I Team, Mitchell Bouchard, Ligency Team | Feb-21 | English | $11.99 | 12h 36m total length | https://www.udemy.com/course/tensorflow-2-practical-advanced/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Google has recently released TensorFlow 2.0 which is Google’s most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning. The global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The technology is progressing at a massive scale and being adopted in almost every sector. The course provides students with practical hands-on experience in training Advanced Artificial Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to: Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces! Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images. Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text! Deploy AI models in practice using TensorFlow 2.0 Serving. Apply Auto-Encoders to perform image compression and de-noising. Apply transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub. The course is targeted towards students wanting to gain a fundamental understanding of how to build, train, test and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and Artificial Neural Networks is recommended. Students who enroll in this course will master Advanced AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems. | https://www.udemy.com/course/tensorflow-2-practical-advanced/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Tensor Flow | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Machine Learning using Python Programming | Learn the core concepts of Machine Learning and its algorithms and how to implement them in Python 3 | 4.4 | 334 | 34668 | Created by Sujithkumar MA | Jun-21 | English | $9.99 | 7h 37m total length | https://www.udemy.com/course/machine-learning-using-python-programming/ | Sujithkumar MA | Engineer | Course Instructor | 4.2 | 4223 | 210381 | 'Machine Learning is all about how a machine with an artificial intelligence learns like a human being' Welcome to the course on Machine Learning and Implementing it using Python 3. As the title says, this course recommends to have a basic knowledge in Python 3 to grasp the implementation part easily but it is not compulsory. This course has strong content on the core concepts of ML such as it's features, the steps involved in building a ML Model - Data Preprocessing, Finetuning the Model, Overfitting, Underfitting, Bias, Variance, Confusion Matrix and performance measures of a ML Model. We'll understand the importance of many preprocessing techniques such as Binarization, MinMaxScaler, Standard Scaler We can implement many ML Algorithms in Python using scikit-learn library in a few lines. Can't we? Yet, that won't help us to understand the algorithms. Hence, in this course, we'll first look into understanding the mathematics and concepts behind the algorithms and then, we'll implement the same in Python. We'll also visualize the algorithms in order to make it more interesting. The algorithms that we'll be discussing in this course are: 1. Linear Regression 2. Logistic Regression 3. Support Vector Machines 4. KNN Classifier 5. KNN Regressor 6. Decision Tree 7. Random Forest Classifier 8. Naive Bayes' Classifier 9. Clustering And so on. We'll be comparing the results of all the algorithms and making a good analytical approach. What are you waiting for? | https://www.udemy.com/course/machine-learning-using-python-programming/#instructor-1 | Languages - C, C++, Python, Verilog, System Verilog Hardware - Digital Logic Design, Computer Architecture, VLSI Design, Analog Electronics, Signal Processing, Embedded Systems Software - Data Structures & Algorithms, Operating Systems, Database Management Systems, Computer Networks, Machine Learning, Deep Learning. Tools - Xilinx Vivado, Matlab, Multisim, Altium, Arduino IDE, TinkerCAD, Tanner EDA, Cadence Virtuoso Boards - Arduino, 8051, TIVA, Raspberry Pi, NodeMCU Areas of Interest - Artificial Intelligence, Digital Design, Software Engineering, Algorithms | Machine Learning | Engineer/Developer | >=4 | Below 1K | >=30K | >=4 | Below 10 K | >=2 Lakh | |||||||||||||||||
Python for Data Science Master Course (2022) | Level up in Data Science using Python, master Numpy, Pandas, Data Visualisation, Web Scraping, Automation, SQL & more.! | 4.3 | 331 | 1913 | Created by Mohit Uniyal, Coding Minutes | Nov-21 | English | $9.99 | 22h 28m total length | https://www.udemy.com/course/python-data-science-master-course/ | Mohit Uniyal | Data Scientist & Coding Minutes Instructor | 4.5 | 1759 | 13798 | Are you ready to take the next leap in your journey to become a Data Scientist? This hands-on course is designed for absolute beginners as well as for proficient programmers who want to use the Python for solving real life problems. You will learn how analyse data, make interesting data visualisations, drive insights, scrape web, automate boring tasks and working with databases using SQL. Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. This course is designed for both beginners with some programming experience or experienced developers looking to enter the world of Data Science! This comprehensive course is taught by Mohit Uniyal, who is a popular Data Science Bootcamp instructor in India and has taught thousands of students in several online and in-person courses over last 3+ years. This course is worth thousands of dollars, but Coding Minutes is providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 5 projects. With over 150+ High Quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science. The course starts with basics of Python and then diving deeper into data science topics! Here are some of the topics that you will learn in this course. Programming with Python Numeric Computation using NumPy Data Analysis using Pandas Data Visualisation using Matplotlib Data Visualisation using Seaborn Fetching data from Web API's Data Acquisition Web Scraping using Beautiful Soup Building a Web Crawler using Scrapy Automating boring stuff using Selenium Language of Databases - SQL! Introduction to Machine Learning and much, much more! Sign up for the course and take your first step towards becoming a data science engineer! See you in the course! | https://www.udemy.com/course/python-data-science-master-course/#instructor-1 | Mohit is a Data Scientist, programming instructor and co-creator at Coding Minutes. He has trained over 20000+ students in Machine Learning and AI over the last 3 years of his teaching experience. His expertise lies in Python, data science, machine learning and AI, and he has won many competitions. He has been doing AI-based projects for the last 4 years. He also leads the ML and Data Science Domain of Coding Minutes. | Python | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Advanced SQL Bootcamp | Take your SQL skills to the next level with our Advanced SQL Bootcamp | 4 | 329 | 4185 | Created by Jose Portilla | Aug-22 | English | $9.99 | 10h 41m total length | https://www.udemy.com/course/advanced-sql-bootcamp/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 973389 | 3123420 | Welcome to the best online course to take your SQL skills to the next level! This course is designed to take you from basic SQL knowledge to a SQL Developer Professional. After completing this course you will have a better understanding of how tables and databases work as well as advanced capabilities in querying the information that is stored in a SQL database effectively. SQL is a critical skill for the modern workforce and we've created an online course specifically designed to take your current SQL knowledge to an advanced level. We'll begin the course with a deep dive into understanding how to construct subqueries and common table expressions, afterwards we'll move on to discussing window functions and advanced Join operations. Then you'll learn about the power of sets, including set operations and grouping sets. We will continue by learning about larger scope topics such as schema structures, table relationships, table inheritance, and views. We'll also teach you how to create easy to call stored procedures and automatic triggers across your database. After each section we will test your skills with a set of exercise questions. In this course, you'll learn everything you need to be an expert SQL developer, we cover the topics that other basics courses don't! In this course you'll learn about: Subqueries Common Table Expressions Window Functions Advanced Join Operations Set Operations Grouping Sets Schema Structures and Table Relationships Table Transactions Views Table Inheritance Stored Procedures Triggers Useful Advanced Methods and much more! Not only does this course come with great technical learning content, but we also provide support in our Q&A Forums inside the course as well as access to our exclusive student community discord server where you can connect with other students. All of this material comes with a 30-day money back guarantee , so you can try the course completely risk free. Enroll today and we'll see you inside the course! | https://www.udemy.com/course/advanced-sql-bootcamp/#instructor-1 | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | SQL | Head/Director | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | |||||||||||||||||
Survival Analysis in R | Use R to master survival analysis, duration analysis, event time analysis or reliability analysis. | 4.4 | 324 | 1661 | Created by R-Tutorials Training | Apr-19 | English | $9.99 | 3h 55m total length | https://www.udemy.com/course/survival-analysis-in-r/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | Survival Analysis is a sub discipline of statistics. It actually has several names. In some fields it is called event-time analysis, reliability analysis or duration analysis. R is one of the main tools to perform this sort of analysis thanks to the survival package. In this course you will learn how to use R to perform survival analysis. To check out the course content it is recommended to take a look at the course curriculum. There are also videos available for free preview. The course structure is as follows: We will start out with course orientation, background on which packages are primarily used for survival analysis and how to find them, the course datasets as well as general survival analysis concepts. After that we will dive right in and create our first survival models. We will use the Kaplan Meier estimator as well as the logrank test as our first standard survival analysis tools. When we talk about survival analysis there is one model type which is an absolute cornerstone of survival analysis: the Cox proportional hazards model. You will learn how to create such a model, how to add covariates and how to interpret the results. You will also learn about survival trees. These rather new machine learning tools are more and more popular in survival analysis. In R you have several functions available to fit such a survival tree. The last 2 sections of the course are designed to get your dataset ready for analysis. In many scenarios you will find that date-time data needs to be properly formatted to even work with it. Therefore, I added a dedicated section on date-time handling with a focus on the lubridate package. And you will also learn how to detect and replace missing values as well as outliers. These problematic pieces of data can totally destroy your analysis, therefore it is crucial to understand how to manage it. Besides the videos, the code and the datasets, you also get access to a vivid discussion board dedicated to survival analysis. By the way, this course is part of a whole data science course portfolio. Check out the R-Tutorials instructor page to see all the other available course. Well over 100.000 people around the world did already use our classes to master data science. Why don´t you try it out yourself? With a Udemy 30-day money back guarantee there is nothing you can lose, you can only gain precious skills to come out ahead in today’s job market. | https://www.udemy.com/course/survival-analysis-in-r/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
The Complete Machine Learning 2022 : 10 Real World Projects | Complete Beginner to Expert Guide-Data Visualization,EDA,Numpy,Pandas,Math,Statistics,Matplotlib,Seaborn,Scikit,NLP-NLTK | 4.7 | 322 | 3584 | Created by MG Analytics | Nov-22 | English | $9.99 | 37h 32m total length | https://www.udemy.com/course/complete-machine-learning-2021-with-10-real-world-projects/ | MG Analytics | Data Scientist and Professional Trainer | 4.5 | 525 | 5351 | Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career. It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model. Our exotic journey will include the concepts of: Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network. What is data science and its need. The need for machine Learning and introduction to NLP (Natural Language Processing). The different types of Machine Learning – Supervised and Unsupervised Learning. Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills. | https://www.udemy.com/course/complete-machine-learning-2021-with-10-real-world-projects/#instructor-1 | I have done B.tech in Computer Science Engineering and 10 + years of experience as a professional instructor and trainer for Data Science and programming. During the course of my career I have developed a skill set in analyzing data and I love sharing my knowledge to help other people learn the power of programming, the ability to analyze data, as well as present the data in clear and beautiful visualizations. I am a Data Scientist and have experience in python, Deep learning, NLP and Big Data. I provide in-person data science, Machine Learning and Deep Learning training to Data science enthusiasts with 0 to 30+ years of Experience. I believe in learning by doing, hence all of my courses will give an in-depth knowledge of concepts followed by detailed explanations of codes, tips and tricks which I have learnt over years. The sample problems and examples will allow you to explore more and give you enough practice to gain confidence at each and every concept. I am here to help you stay on the cutting edge of Data Science and Technology. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
SQL for Tech and Data Science Interviews | Do you want to ace your SQL tech or data science interview? This Is the perfect resource for you! | 4.5 | 321 | 2923 | Created by 365 Careers, Tina Huang | Jun-21 | English | $9.99 | 2h 27m total length | https://www.udemy.com/course/sql-for-tech-and-data-science-interviews/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | You want to make sure you are ready for your tech/data science interview? Do you want to learn from an instructor who works for one of the FAANG companies? Great! Perhaps it was chance that brought you here, but now that you have found this resource, you are a step closer to building a deliberate strategy. You will learn how to focus on doing mock interviews with real interview questions and in a real interview style. Please bear in mind that this is not an Intro to SQL course that goes in depth about every single concept and function available for you to work with. Instead, this is a resource that will help you ace the job interview and get hired, provided that you have already learned the SQL basics. If you are looking for a guided walkthrough and coaching through 10 mock interviews – this is the right course for you. Tina’s 5-step framework will prepare you to tackle any SQL interview question. You will find out what interviewers want to hear from you. Learning how to interact with them is a fundamental skill you will need to master. Oftentimes, potential employers will challenge your assumptions and ask for your thoughts simply because they want to see how you deal with unexpected situations and challenges. You must find a way to keep the conversation going and know how to shrug off any mistakes you make throughout the interview! The goal of this course is for you to have done the SQL interview at least 10 times – so, when the real interview comes, it just feels like another practice round. About the author: Tina Huang is a data scientist at one of the FAANG companies. She is also a popular YouTuber with over 60k subscribers. Tina is proud she taught herself SQL from scratch in 11 days to pass her FAANG SQL interview. This course is one of the best resources you can choose to prepare yourself for the SQL interview you need, to land a job in tech and data science! | https://www.udemy.com/course/sql-for-tech-and-data-science-interviews/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | SQL | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||||
Machine Learning : A Beginner's Basic Introduction | Learn Machine Learning Basics with a Practical Example | 3.9 | 321 | 25365 | Created by Bluelime Learning Solutions | Jun-21 | English | $9.99 | 1h 47m total length | https://www.udemy.com/course/machine-learning-a-beginners-basic-introduction/ | Bluelime Learning Solutions | Learning made simple | 4.1 | 36082 | 742296 | Machine learning relates to many different ideas, programming languages, frameworks. Machine learning is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules or algorithms and learn about new things, on its own. In this course, we'll explore some basic machine learning concepts and load data to make predictions. Value estimation—one of the most common types of machine learning algorithms—can automatically estimate values by looking at related information. For example, a website can determine how much a house is worth based on the property's location and characteristics. In this course, we will use machine learning to build a value estimation system that can deduce the value of a home. Although the tool we will build in this course focuses on real estate, you can use the same approach to solve any kind of value estimation. What you'll learn include: Basic concepts in machine learning Supervised versus Unsupervised learning Machine learning frameworks Machine learning using Python and scikit-learn Loading sample dataset Making predictions based on dataset Setting up the development environment Building a simple home value estimator The examples in this course are basic but should give you a solid understanding of the power of machine learning and how it works. | https://www.udemy.com/course/machine-learning-a-beginners-basic-introduction/#instructor-1 | Bluelime is UK based and creates quality easy to understand eLearning solutions .All our courses are 100% video based. We teach hands –on- examples that teach real life skills . Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need. | Machine Learning | >=3 | Below 1K | >=25K | >=4 | Below 1 Lakh | >=5 Lakh | ||||||||||||||||||
Logistic Regression in R Studio | Logistic regression in R Studio tutorial for beginners. You can do Predictive modeling using R Studio after this course. | 4.7 | 319 | 84893 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 6h 16m total length | https://www.udemy.com/course/machine-learning-basics-classification-models-in-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Classification modeling course that teaches you everything you need to create a Classification model in R, right? You've found the right Classification modeling course covering logistic regression, LDA and kNN in R studio! After completing this course, you will be able to: · Identify the business problem which can be solved using Classification modeling techniques of Machine Learning. · Create different Classification modelling model in R and compare their performance. · Confidently practice, discuss and understand Machine Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular Classification techniques of machine learning, such as Logistic Regression, Linear Discriminant Analysis and KNN Why should you choose this course? This course covers all the steps that one should take while solving a business problem using classification techniques. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a classification model, to solve business problems. Below are the course contents of this course on Logistic Regression: · Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation · Section 2 - R basic This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. · Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. · Section 4 - Data Pre-processing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment and missing value imputation. · Section 5 - Classification Models This section starts with Logistic regression and then covers Linear Discriminant Analysis and K-Nearest Neighbors. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models performance using confusion matrix, how categorical variables in the independent variables dataset are interpreted in the results, test-train split and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a classification model in R will soar. You'll have a thorough understanding of how to use Classification modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Which all classification techniques are taught in this course? In this course we learn both parametric and non-parametric classification techniques. The primary focus will be on the following three techniques: Logistic Regression Linear Discriminant Analysis K - Nearest Neighbors (KNN) How much time does it take to learn Classification techniques of machine learning? Classification is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn classification starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of classification. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use R for Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-basics-classification-models-in-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Misc | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Machine Learning with Python from Scratch | Mastering Machine Learning Algorithms including Neural Networks with Numpy, Pandas, Matplotlib, Seaborn and Scikit-Learn | 3.5 | 315 | 4452 | Created by Tim Buchalka's Learn Programming Academy, CARLOS QUIROS | Jan-21 | English | $9.99 | 12h 38m total length | https://www.udemy.com/course/python-machine-learning/ | Tim Buchalka's Learn Programming Academy | Professional Programmers and Teachers - 1.68M students | 4.5 | 480124 | 1685357 | Machine Learning is a hot topic! Python Developers who understand how to work with Machine Learning are in high demand. But how do you get started? Maybe you tried to get started with Machine Learning, but couldn’t find decent tutorials online to bring you up to speed, fast. Maybe the information you found was too basic, and didn’t give you the real-world Machine learning skills using Python that you needed. Or maybe the information got bogged down in complex math explanations and was too difficult to relate to. Whatever the reason, you are in the right place if you want to progress your skills in Machine Language using Python. This course will help you to understand the main machine learning algorithms using Python, and how to apply them in your own projects. But what exactly is Machine Learning? It’s a field of computer science that gives computers the ability to “learn” – e.g. continually improve performance on a specific task, with data, without being explicitly programmed. Why is it important? Machine learning is often used to solve tasks considered too complex for humans to solve. We create algorithms and apply a bunch of data to that algorithm and let the computer process (execute) the algorithm and search for a model (solution). Because of the practical applications of machine learning, such as self driving cars (one example) there is huge interest from companies and government in Machine learning, and as a result, there are a a lot of opportunities for Python developers who are skilled in this field. If you want to increase your career options, then understanding and being able to work with Machine Learning with your own Python programs should be high on your list of priorities. What will you learn in this course? For starters, you will learn about the main scientific libraries in Python for data analysis such as Numpy, Pandas, Matplotlib and Seaborn. You’ll then learn about artificial neural networks and how to work with machine learning models using them. You obtain a solid background in machine learning and be able to apply that knowledge directly in your own programs. What are the Main topics included in the course? Data Analysis with Numpy, Pandas, Matplotlib and Seaborn. The machine learning schema. Overfitting and Underfitting K Fold Cross Validation Classification metrics Regularization: Lasso, Ridge and ElasticNet Logistic Regression Support Vector Machines for Regression and Classification Naive Bayes Classifier Decision Trees and Random Forest KNN classifier Hyperparameter Optimization: GridSearchCV Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Kernel Principal Component Analysis (KPCA) Ensemble methods: Bagging AdaBoost K means clustering analysis Regression model and evaluation Linear and Polynomial Regression SVM, KNN, and Random Forest for Regression RANSAC Regression Neural Networks: Constructing our own MLP. Perceptron and Multilayer Perceptron And don’t worry if you do not understand some, or all of these terms. By the end of the course you will know what they are and how to use them. Why enrolling in this course is the best decision you can make. This course helps you to understand the difficult concepts of Machine learning in a unique way. Rather than just focusing on complex maths explanaitons, simpler explanations with charts, and info displays are included. Many examples and genuinely useful code snippets are also included to make it even easier to learn and understand. After completing this course, you will have the necessary skills to apply Machine learning in your own projects. The sooner you sign up for this course, the sooner you will have the skills and knowledge you need to increase your job or consulting opportunities. Your new job or consulting opportunity awaits! Why not get started today? Click the Signup button to sign up for the course! | https://www.udemy.com/course/python-machine-learning/#instructor-1 | The Learn Programming Academy was created by Tim Buchalka, a software developer with 35 years experience, who is also an instructor on Udemy, with over 1.68M+ students in his courses on Java, Python, Android, C# and the Spring framework. The Academy’s goal in the next three years, is to teach one million people to learn how to program. Apart from Tim’s own courses, which are all available here, we are working with the very best teachers, creating courses to teach the essential skills required by developers, at all levels. One other important philosophy is that our courses are taught by real professionals; software developers with real and substantial experience in the industry, who are also great teachers. All our instructors are experienced, software developers! Our team is busy creating new courses right now. Whether you are a beginner, looking to learn how to program for the very first time, or to brush up on your existing skills, or to learn new languages and frameworks, the Academy has you covered. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | Below 10K | >=4 | >=4.5 Lakh | >=10 Lakh | |||||||||||||||||
Bayesian Computational Analyses with R | Learn the concepts and practical side of using the Bayesian approach to estimate likely event outcomes. | 4.6 | 315 | 3625 | Created by Geoffrey Hubona, Ph.D. | Sep-20 | English | $11.99 | 11h 37m total length | https://www.udemy.com/course/bayesian-computational-analyses-with-r/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences about the likely parameters of the population. In contrast, the Bayesian approach uses both likelihood functions and a sample of observed data (the 'prior') to estimate the most likely values and distributions for the estimated population parameters (the 'posterior'). The course is useful to anyone who wishes to learn about Bayesian concepts and is suited to both novice and intermediate Bayesian students and Bayesian practitioners. It is both a practical, "hands-on" course with many examples using R scripts and software, and is conceptual, as the course explains the Bayesian concepts. All materials, software, R scripts, slides, exercises and solutions are included with the course materials. It is helpful to have some grounding in basic inferential statistics and probability theory. No experience with R is necessary, although it is also helpful. The course begins with an introductory section (12 video lessons) on using R and R 'scripting.' The introductory section is intended to introduce RStudio and R commands so that even a novice R user will be comfortable using R. Section 2 introduces the Bayesian Rule, with examples of both discrete and beta priors, predictive priors, and beta posteriors in Bayesian estimation. Section 3 explains and demonstrates the use of Bayesian estimation for single parameter models, for example, when one wishes to estimate the most likely value of a mean OR of a standard deviation (but not both). Section 4 explains and demonstrates the use of "conjugate mixtures." These are single-parameter models where the functional form of the prior and post are similar (for example, both normally distributed). But 'mixtures' imply there may be more than one component for the prior or posterior density functions. Mixtures enable the simultaneous test of competing, alternative theories as to which is more likely. Section 5 deals with multi-parameter Bayesian models where one is estimating the likelihood of more than one posterior variable value, for example, both mean AND standard deviation. Section 6 extends the Bayesian discussion by examining the estimation of integrals to estimate a probability. Section 7 covers the application the Bayesian approach to rejection and importance sampling and Section 8 looks at examples of comparing and validating Bayesian models. | https://www.udemy.com/course/bayesian-computational-analyses-with-r/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Statistics in R - The R Language for Statistical Analysis | Statistics made easy with the open source R language. Learn about Regression, Hypothesis tests, R Commander ... | 4.3 | 313 | 3249 | Created by R-Tutorials Training | Nov-18 | English | $9.99 | 4h 10m total length | https://www.udemy.com/course/statisticsinr/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | Do you want to learn more about statistical programming? Are you in a quantitative field? You want to know how to perform statistical tests and regressions? Do you want to hack the learning curve and stay ahead of your competition? If YES came to your mind to some of those points - read on! This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R. While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions. After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R. Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions. According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own. Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming. What R you waiting for? Martin | https://www.udemy.com/course/statisticsinr/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Statistics | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Neural Networks in Python from Scratch: Complete guide | Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice! | 4.7 | 313 | 2716 | Created by Jones Granatyr, Ligency I Team, Ligency Team, IA Expert Academy | Feb-22 | English | $11.99 | 8h 41m total length | https://www.udemy.com/course/neural-networks-in-python-a-guide-for-beginners/ | Jones Granatyr | Professor | 4.7 | 34416 | 158021 | Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis. One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. This is due to the fact that the majority of the materials that are available are very technical and apply a lot of mathematical formulas, which simply makes the learning process incredibly difficult for whomever wishes to take their first steps in this field. With this in mind, the main objective of this course is to present the theoretical and mathematical concepts of neural networks in a simple yet thorough way, so even if you know nothing about neural networks, you’ll understand all the processes. We’ll cover concepts such as perceptrons, activation functions, multilayer networks, gradient descent and backpropagation algorithms, which form the foundations through which you will understand fully how a neural network is made. We’ll also cover the implementations on a step-by-step basis using Python, which is one of the most popular programming languages in the field of Data Science. It’s important to highlight that the step-by-step implementations will be done without using Machine Learning-specific Python libraries, because the idea behind this course is for you to understand how to do all the calculations necessary in order to build a neural network from scratch. To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts. Are you ready to take the next step in your professional career? I’ll see you in the course! | https://www.udemy.com/course/neural-networks-in-python-a-guide-for-beginners/#instructor-1 | Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios. | Neural Networks | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Machine Learning Optimization Using Genetic Algorithm | Learn how to optimize Machine Learning algorithms' performances and apply feature selection using Genetic Algorithm | 4.5 | 306 | 2628 | Created by Curiosity for Data Science | Aug-20 | English | $12.99 | 6h 33m total length | https://www.udemy.com/course/machine-learning-optimization-using-genetic-algorithm/ | Curiosity for Data Science | Architect and Industrial Engineer | 4.1 | 1565 | 8648 | In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines (SVMs) and Multilayer Perceptron Neural Networks (MLP NNs). It is referred to as hyperparameter tuning or parameter tuning. You will also learn how to do feature selection using Genetic Algorithm. Hyperparameter optimization will be done on two datasets: A regression dataset for the prediction of cooling and heating loads of buildings A classification dataset regarding the classification of emails into spam and non-spam The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization Feature Selection will be done on one dataset: Classification of benign tumors from malignant tumors in a breast cancer dataset By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your machine learning algorithms for maximum performance. You would have also learnt how to apply Genetic Algorithm for feature selection. To sum up: You will learn what hyperparameters are (sometimes referred to as parameters, though different) You will learn Genetic Algorithm You will use Genetic Algorithm to optimize the performance of your machine learning algorithms Maximize your model's accuracy and predictive abilities Optimize the performance of SVMs and MLP Neural Networks Apply feature selection to extract the features that are relevant to the predicted output Get the best out of your machine learning model Remove redundant features, which in return will reduce the time and complexity of your model Understand what are the features that have a relationship to the output and which do not You do not need to have a lot of knowledge and experience in optimization or Python programming - it helps, but not a must to succeed in this course. This course will teach you how to optimize the functionality of your machine learning algorithms Where every single line of code is explained thoroughly The code is written in a simple manner that you will understand how things work and how to code Genetic Algorithm even with zero knowledge in Python Basically, you can think of this as not only a course that teaches you how to optimize your machine learning model, but also Python programming! Please feel free to ask me any question! Don't like the course? Ask for a 30-day refund!! Real Testaments --> 1) "This is my second course with Dana. This course is a combination of Metaheuristic and machine learning. It gives a wide picture of machine learning hyperparameter optimization. I recommend taking this course if you know basics of machine learning and you want to solve some problems using ML. By applying the techniques of GA optimization, you will have better performance of ML. The codes provided in this course are very straightforward and easy to understand. The course deserves five stars because of the lecture contents and examples. The instructor knowledgeable about the topic and talented in programming." -- Abdulaziz, 5 star rating 2) "An excellent course! Great for anyone interested in fine-tuning their machine-learning models. I really enjoyed the from scratch implementations and how well they are explained. These implementations from scratch help one understand the theory very well. An interesting thing to point out is that this course uses Metaheustistics to optimise machine-learning. However, you can use machine-learning classifiers to help your Metaheuristic predict good or bad regions." -- Dylan, 5 star rating 3) "Very helpful, for application of optimization algorithm to optimize ML algorithm parameters and got to do this using python, wonderful." -- Erigits, 5 star rating 4) "well explained course. The topic is not an easy one but to date the explanations have been clear. The course has an interesting spreadsheet project." -- Martin, 5 star rating 5) "Thank you very much for this awesome course. Lots of new things learn from this course." -- Md. Mahmudul, 5 star rating | https://www.udemy.com/course/machine-learning-optimization-using-genetic-algorithm/#instructor-1 | Hi! I'm Dana. I'm currently a PhD student in Industrial Engineering. I finished my B.S. in Architectural Engineering and my M.S. in Industrial Engineering. Lean Six Sigma Green Belt certified. I enjoy learning new things. My research interest is Optimization and Data Science including Deep Learning, Machine Learning, and Artificial Intelligence. My areas of expertise include Python Programming, Data Science, Machine Learning, and Optimization using Metaheuristics. | Machine Learning | Architect | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | |||||||||||||||||
ML for Business Managers: Build Regression model in R Studio | Simple Regression & Multiple Regression| must-know for Machine Learning & Econometrics | Linear Regression in R studio | 4.4 | 305 | 74382 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 6h 22m total length | https://www.udemy.com/course/machine-learning-basics-building-a-regression-model-in-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in R, right? You've found the right Linear Regression course! After completing this course you will be able to: · Identify the business problem which can be solved using linear regression technique of Machine Learning. · Create a linear regression model in R and analyze its result. · Confidently practice, discuss and understand Machine Learning concepts A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: · Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation · Section 2 - R basic This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. · Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. · Section 4 - Data Preprocessing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. · Section 5 - Regression Model This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in R will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the R environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in R Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture in R where we actually run each query with you. Why use R for data Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-basics-building-a-regression-model-in-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Misc | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
TensorFlow and the Google Cloud ML Engine for Deep Learning | CNNs, RNNs and other neural networks for unsupervised and supervised deep learning | 4.3 | 305 | 4140 | Created by Loony Corn | Jul-18 | English | $9.99 | 17h 23m total length | https://www.udemy.com/course/from-0-to-1-tensorflow-for-deep-learning/ | Loony Corn | An ex-Google, Stanford and Flipkart team | 4.2 | 26022 | 153513 | TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming. What's covered: Deep learning basics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network models Using Deep Learning for the famous ML problems: regression, classification, clustering and autoencoding CNNs - Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradients Unsupervised learning techniques - Autoencoding, K-means clustering, PCA as autoencoding Working with images Working with documents and word embeddings Google Cloud ML Engine: Distributed training and prediction of TF models on the cloud Working with TensorFlow estimators | https://www.udemy.com/course/from-0-to-1-tensorflow-for-deep-learning/#instructor-1 | Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you'll like them 🙂 | Deep Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||||
' Web scraping : Python Beautiful Soup Web scraping Bootcamp | Beginner friendly and Project based web scraping . Python Beautiful Soup web scraping for Data Science & Data Analysis . | 3.5 | 298 | 26471 | Created by Shepherd Mahupa | Jul-20 | English | $9.99 | 1h 22m total length | https://www.udemy.com/course/python-beautifulsoup-webscraping-for-data-science-projects/ | Shepherd Mahupa | Data Scientist | 3.5 | 387 | 33103 | Beginner-Friendly and Projects-Based Learning Beginner-friendly and project-based learning content is hard to find on the web. This course is designed for you to start from the zero-knowledge that you have on Web scraping and a little of Python and Data Science to working on real-life projects and building your portfolio. Why Web scraping (Application and Case Studies) The first part of the course focuses on how web scrapping is applied in different industries to bring value. Web Scraping is a tool for automating the collection of data or building datasets for analysis and modelling. If you are looking forward to mining data on the internet in your job or to start a business that applies this tool, this course will bring more light on how to do it. Web scraping Process Before going into detail, there is a summary of how to approach web scraping. Once you understand the thought process, you will be able to tackle challenging projects. Its always good to know the fundamentals before going into the application part. Requests & Beautiful Soup Libraries These are the libraries that you are going to learn in this tutorial for web scraping. They are taught from scratch and you don't need lots of python programming skills to master them. Basic knowledge of python is essential. Python Pandas Library: Building a DataFrame To do further analysis and modelling you must build a dataset. By using the Python Pandas library, you can build a DataFrame that you will use for your analysis or machine learning models. Python Web Scraping Projects The projects in this tutorial contain various concepts that are key in web scraping. After these projects, you will be able to tackle your own projects and solve challenges on your own. You are simultaneously building a little portfolio as you work on these projects. | https://www.udemy.com/course/python-beautifulsoup-webscraping-for-data-science-projects/#instructor-1 | I am on Udemy because I believe anybody who wants to learn something new needs to be supported with beginner-friendly and project-based learning content. I believe if you master the fundamentals, you can venture into any field that you want. My courses follow this thought process and I apply it in my life as well. Teaching is my passion. | Python | Data Scientist | >=3 | Below 1K | >=25K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Decision Tree - Theory, Application and Modeling using R | Analytics/ Supervised Machine Learning/ Data Science: CHAID / CART / Random Forest etc. workout (Python demo at the end) | 4.1 | 295 | 1839 | Created by Gopal Prasad Malakar | Jan-21 | English | $9.99 | 8h 1m total length | https://www.udemy.com/course/decision-tree-theory-application-and-modeling-using-r/ | Gopal Prasad Malakar | Trains Industry Practices on data science / machine learning | 4.2 | 10737 | 111703 | What is this course? Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building. This course ensures that student get understanding of what is the decision tree where do you apply decision tree what benefit it brings what are various algorithm behind decision tree what are the steps to develop decision tree in R how to interpret the decision tree output of R Course Tags Decision Tree CHAID CART Objective segmentation Predictive analytics ID3 GINI Material in this course the videos are in HD format the presentation used to create video are available to download in PDF format the excel files used is available to download the R program used is also available to download How long the course should take? It should take approximately 8 hours to internalize the concepts and become comfortable with the decision tree modeling using R The structure of the course Section 1 – motivation and basic understanding Understand the business scenario, where decision tree for categorical outcome is required See a sample decision tree – output Understand the gains obtained from the decision tree Understand how it is different from logistic regression based scoring Section 2 – practical (for categorical output) Install R - process Install R studio - process Little understanding of R studio /Package / library Develop a decision tree in R Delve into the output Section 3 – Algorithm behind decision tree GINI Index of a node GINI Index of a split Variable and split point selection procedure Implementing CART Decision tree development and validation in data mining scenario Auto pruning technique Understand R procedure for auto pruning Understand difference between CHAID and CART Understand the CART for numeric outcome Interpret the R-square meaning associated with CART Section 4 – Other algorithm for decision tree ID3 Entropy of a node Entropy of a split Random Forest Method Why take this course? Take this course to Become crystal clear with decision tree modeling Become comfortable with decision tree development using R Hands on with R package output Understand the practical usage of decision tree | https://www.udemy.com/course/decision-tree-theory-application-and-modeling-using-r/#instructor-1 | I am a seasoned Analytics professional with 20+ years of professional experience. I have industry experience of impactful and actionable analytics, data science, decision strategy and enterprise wise data strategy. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting, business intelligence systems like tableau /SAS Visual Analytics, MS access based database application development, Enterprise wide big data framework and streaming analysis. Please refer to my course for - SAS / R program details (syntax and options) - SAS / R output deep dive - Practical usage in Industrial situation | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1 Lakh | ||||||||||||||||||
Apache Spark 3 - Real-time Stream Processing using Scala | Learn to create Real-time Stream Processing applications using Apache Spark | 4.6 | 295 | 6334 | Created by Prashant Kumar Pandey, Learning Journal | Jul-21 | English | $9.99 | 4h 18m total length | https://www.udemy.com/course/apache-spark-streaming-in-scala/ | Prashant Kumar Pandey | Architect, Author, Consultant, Trainer @ Learning Journal | 4.6 | 14112 | 79992 | About the Course I am creating Apache Spark 3 - Real-time Stream Processing using the Scala course to help you understand the Real-time Stream processing using Apache Spark and apply that knowledge to build real-time stream processing solutions. This course is example-driven and follows a working session like approach. We will be taking a live coding approach and explain all the needed concepts along the way. Who should take this Course? I designed this course for software engineers willing to develop a Real-time Stream Processing Pipeline and application using the Apache Spark. I am also creating this course for data architects and data engineers who are responsible for designing and building the organization’s data-centric infrastructure. Another group of people is the managers and architects who do not directly work with Spark implementation. Still, they work with the people who implement Apache Spark at the ground level. Spark Version used in the Course This Course is using the Apache Spark 3.x. I have tested all the source code and examples used in this Course on Apache Spark 3.0.0 open-source distribution. | https://www.udemy.com/course/apache-spark-streaming-in-scala/#instructor-1 | Prashant Kumar Pandey is passionate about helping people to learn and grow in their career by bridging the gap between their existing and required skills. In his quest to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. With over 18 years of experience in IT as a developer, architect, consultant, trainer, and mentor, he has worked with international software services organizations on various data-centric and Bigdata projects. Prashant is a firm believer in lifelong continuous learning and skill development. To popularize the importance of lifelong continuous learning, he started publishing free training videos on his YouTube channel and conceptualized the idea of creating a Journal of his learning under the banner of Learning Journal. He is the founder, lead author, and chief editor of the Learning Journal portal that offers various skill development courses, training, and technical articles since the beginning of the year 2018. | Scala | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Complete iOS Machine Learning Masterclass | The most comprehensive course on Machine Learning for iOS development. Master building smart apps iOS Swift 4 | 4.5 | 293 | 16724 | Created by Yohann Taieb | Aug-17 | English | $9.99 | 7h 34m total length | https://www.udemy.com/course/complete-ios-machine-learning-masterclass/ | Yohann Taieb | Apps Games Unity iOS Android Apple Watch TV Development | 3.9 | 9287 | 260851 | If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass™ is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you’ll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We’re approaching a new era where only apps and games that are considered “smart” will survive. (Remember how Blockbuster went bankrupt when Netflix became a giant?) Jump the curve and adopt this innovative approach; the Complete iOS Machine Learning Masterclass™ will introduce Machine Learning in a way that’s both fun and engaging. In this course, you will: Master the 3 fundamental branches of applied Machine Learning: Image & Video Processing, Text Analysis, and Speech & Language Recognition Develop an intuitive sense for using Machine Learning in your iOS apps Create 7 projects from scratch in practical code-along tutorials Find pre-trained ML models and make them ready to use in your iOS apps Create your own custom models Add Image Recognition capability to your apps Integrate Live Video Camera Stream Object Recognition to your apps Add Siri Voice speaking feature to your apps Dive deep into key frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit. Use Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder–even if you have zero experience Get FREE unlimited hosting for one year And more! This course is also full of practical use cases and real-world challenges that allow you to practice what you’re learning. Are you tired of courses based on boring, over-used examples? Yes? Well then, you’re in a treat. We’ll tackle 5 real-world projects in this course so you can master topics such as image recognition, object recognition, and modifying existing trained ML models. You’ll also create an app that classifies flowers and another fun project inspired by Silicon Valley™ Jian Yang’s masterpiece: a Not-Hot Dog classifier app! Why Machine Learning on iOS One of the hottest growing fields in technology today, Machine Learning is an excellent skill to boost your your career prospects and expand your professional tool kit. Many of Silicon Valley’s hottest companies are working to make Machine Learning an essential part of our daily lives. Self-driving cars are just around the corner with millions of miles of successful training. IBM’s Watson can diagnose patients more effectively than highly-trained physicians. AlphaGo, Google DeepMind’s computer, can beat the world master of the game Go, a game where it was thought only human intuition could excel. In 2017, Apple has made Machine Learning available in iOS so that anyone can build smart apps and games for iPhones, iPads, Apple Watches and Apple TVs. Nowadays, apps and games that do not have an ML layer will not be appealing to users. Whether you wish to change careers or create a second stream of income, Machine Learning is a highly lucrative skill that can give you an amazing sense of gratification when you can apply it to your mobile apps and games. Why This Course Is Different Machine Learning is very broad and complex; to navigate this maze, you need a clear and global vision of the field. Too many tutorials just bombard you with the theory, math, and coding. In this course, each section focuses on distinct use cases and real projects so that your learning experience is best structured for mastery. This course brings my teaching experience and technical know-how to you. I’ve taught programming for over 10 years, and I’m also a veteran iOS developer with hands-on experience making top-ranked apps. For each project, we will write up the code line by line to create it from scratch. This way you can follow along and understand exactly what each line means and how to code comes together. Once you go through the hands-on coding exercises, you will see for yourself how much of a game-changing experience this course is. As an educator, I also want you to succeed. I’ve put together a team of professionals to help you master the material. Whenever you ask a question, you will get a response from my team within 48 hours. No matter how complex your question, we will be there–because we feel a personal responsibility in being fully committed to our students. By the end of the course, you will confidently understand the tools and techniques of Machine Learning for iOS on an instinctive level. Don’t be the one to get left behind. Get started today and join millions of people taking part in the Machine Learning revolution. topics: ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection ios swift 4 coreml vision deep learning machine learning neural networks python anaconda trained models keras tensorflow scikit learn core ml ios12 Swift4 scikitlearn artificial neural network ANN recurrent neural network RNN convolutional neural network CNN ocr character recognition face detection | https://www.udemy.com/course/complete-ios-machine-learning-masterclass/#instructor-1 | Yohann holds a Bachelor of Science Degree in Computer Science from FIU University. He has been a College instructor for over 15 years, teaching iPhone Development, iOS 15, Apple Watch development, Swift 5, Unity 3D, Pixel Art, Photoshop for programmers, Android and blockchain development. Yohann also has plenty of ideas which naturally turned him into an entrepreneur, where he owns over 100 mobile apps and games in both the Apple app store and the Android store. Yohann is one of the leading experts in mobile game programming, app flipping and reskinning. His teaching style is unique, hands on and very detailed. Yohann has enabled more than 250000 students to publish their own apps and reach the top spots in iTunes App Stores, which has been picked up by blogs and medias like WIRED magazine, Yahoo News, and Forbes Online. Thanks to him, thousands of students now make a living using iOS 15, Swift 4, Objective C ( ObjC ), Machine Learning, Augmented Reality / VIrtual Reality, Android, Apple Watch ( watchOS ), Apple TV ( TVOS ), Unity 3D, and Pixel art animation | Machine Learning | >=4 | Below 1K | >=15K | >=3 | Below 10 K | >=2.5 Lakh | ||||||||||||||||||
Talend + SQL + Datawarehousing - Beginner to Professional | Talend + SQL + Datawarehousing | 4.7 | 292 | 1955 | Created by TalendTech Solutions | Jul-22 | English | $159.99 | 13h 16m total length | https://www.udemy.com/course/talendsql/ | TalendTech Solutions | ETL Talend Techie turned Trainer | 4.7 | 292 | 1955 | Talend is an Open Source/Enterprise ETL Tool, which can be used by Small to Large scale companies to perform Extract Transform and Load their data into Databases or any File Format (Talend supports almost all file formats and Database vendors available in the market including Cloud and other niche services). This Course is for anyone who wants to learn Talend from Beginner to Professional, it will also help in Enhancing your skills if you have prior experience with the tool. In the course we teach Talend - ETL tool, PostgreSQL - SQL and all the basic Datawarehousing concepts that you would need to work and excel in the organization or freelance. We give real world scenarios and try to explain the use of component so that it becomes more relevant and useful for your real world projects. Prepares you for the Certification Exam. By the end of the Course you will become the Master in Talend Data Intergration and will help you land the job as ETL or Talend Developer, which is high in demand. Prerequisites ? Basic Knowledge of working on PC Target Audience ? Anyone who wants to enter the IT industry from non technical background. Anyone who wants to enhance their concepts of Talend Studio to perform data integration. Anyone who wants to get job as a Talend Developer. Anyone who wants to learn basics of SQL. Anyone who wants to learn basics of datawarehousing. System Requirements ? PC or lappy with preferably more than 4GB RAM and i3 above processor. Talend Open Studio Software - FREE for everyone PostgreSQL Database - FREE for local implementation | https://www.udemy.com/course/talendsql/#instructor-1 | TalendTech Solutions (Sanjay & Tyagi), having 10+ years of experience in Talend ETL Tool, presents Talend + SQL + Datawarehousing course and train professionals to become job ready as ETL developer/Data Engineer. Worked on Data warehouse, Data migration, Data lake projects using talend as ingestion and ETL tool. Expert in Talend framework jobs and optimising the talend jobs. | SQL | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Science and Machine Learning with Python and Libraries | Learn Data Science and Machine Learning with Python and Libraries such as Numpy, Matplotlib, Pandas and much more | 4.3 | 292 | 2057 | Created by The Tech X Learners | Sep-22 | English | $9.99 | 27h 7m total length | https://www.udemy.com/course/data-science-and-machine-learning-with-python-and-libraries-t/ | The Tech X Learners | Artificial Intelligence Startup | 4.5 | 415 | 3801 | WHAT IS DATA SCIENCE As the world is progressing in science and technology, there is an enormous increase in the need for advanced tools to store information and mine data that is being produced indefinitely. And the key to this problem is data science. Data science is a field of study that develops scientific and systematic methods to record, process and analyze data to withdraw significant and useful information that can be both structured and unstructured. Unstructured data is the one that is generated by mobile devices and websites while structured data is an organized data which is mostly generated by the users e.g. emails, chats, telephone calls etc. Data science uses scientific methods and algorithms to extract knowledge. Industries require the use of this field immensely and the industrialists now realize the value of data science and the benefits it can provide to the business, thus, it has become very popular currently. The need for data science An immediate question that rises in the mind after hearing about data science is why is there a need to dig into depths for such a tool? So, it’s important to understand that previously the data produced used to be structured and thus it was somehow easy to extract the meaningful information and process it. However, contemporarily the data that is being produced is mostly unstructured as there are multiple sources of its generation such as multimedia files, logs, documents etc. and data science provides aid in turning raw data into consequential one. Human brains possess the intelligence to perceive things as they are i.e. processing the information that we see and store it. It is just a power that we humans have and because we are doing it constantly without any deliberate effort, it is trivial to us. However, parallel to this brilliant power that we hold, it is also to have a clear understanding of the fact that it is limited. Human brain is also prone to forgetting, and impeding memory, perceptions and predictions. Here, the computer’s prowess proves to be helpful. With the advance improvement in technology, we are now able to leave our locations through our smartphones for example uber that makes our traveling so much convenient, our movements can be tracked easily, our online behaviors and patterns of search are constantly being recorded through our acceptance of cookies, the steps we take during a day can be tracked by the help of some specific apps, even our health can be tracked. All of this is possible due to the invention of technology, softwares and the ability to record and process information adequately. Our information is being stored and without us even noticing. This tracking ability is not only applicable on the people sitting at home, but government agencies, stock markets, civil officers, intelligence agencies, business owners are all the concerned parties and is useful for each one of the occupations. Data science is used in providing systematic methods to give useful insights from the enormous data that is being generated. Therefore, data science is indeed important. Other than the general importance, data science is extremely beneficial to the business in numerous ways: · It provides immense help in decision taking. As discusses, data science analyzes the data in a proper way to solve the problems arising in the business by providing healthy opportunities and unleashing ideas to remove the threats creating issues for the business. It foresees the trends that might surface in the future. · Data science helps in the business to flourish by improving the product in every which way possible. By tracking the patterns of the consumer’s purchase and knowing about the likes and dislikes, the managers will know what improvisations are required, what kind of product is outdated and what basically is the trend prevailing. Also, by tracing the online trends, the business will be better able to identify the wants of the consumers and produce accordingly to satisfy their needs in an ample manner. · Similarly, the business will be able to be managed efficiently. Due to the help of data science, the owners will be able to understand the needs of the customers in better way and this will lead to increasing the number of customers. Satisfying the customers efficiently will result in business optimization. · Data science owns the capability of predicting trends which proves to be a great benefit to the business. To be able to read the patterns and predict the future tendency of peoples’ wants is going to be lucrative in every which way possible. · Advertisement holds are huge part in the business being able to thrive and the product reaching its target audience. Data science helps in better marketing. Companies need good marketing strategies every day and they analyze their data to create impactful advertisements. Data science can make it easier by making smarter decisions for them and run a campaign for them for the specific purpose. · Data science holds the future. Industries are becoming data driven and they need data scientists to process and analyze the data for them and make smarter decision by predicting information. Therefore, it holds the career for tomorrow. · Reading resumes and appointing the right person for the job is a daily task in a company. This exhausting task can be done easily and efficiently through the amount of data available online. Social media and job search websites can be searched thoroughly by the data scientists and select the perfect candidate according to his talents and capabilities. · Data science can also provide beneficial aid in identifying the right target audience. Data science can prove to be helpful in collecting customer data and gaining insights onto their liking and disliking. The company can learn more about their audience and gain in depth knowledge to target the right group of audience and increase profit margins. Advantages and disadvantages Data science is a highly prestigious and versatile career. It also holds great scope in personal growth. It is highly in demand and it holds the future. All the industries realize the importance of the field and all the benefits that it can reap for them and is held as an important position for the company, so it is a highly paid profession. The job is extremely interesting. There are no repetitive tasks to be performed and thus it is not boring at all. Data science is a field that aims in making data meaningful for the company by improving its quality. It makes computers smart enough to read the behaviors and patterns of the customers though their historical purchases and search history. This machine learning phenomenon helps the company in producing better products. However, the field has its disadvantages as well. Data science is a very vague term and it is not easily understandable. Mastering the degree of data science is nearly impossible. To hold proficiency in the field, you require large amount of domain knowledge. Data science helps in predicting future trends but sometimes the results do not yield to be the one as expected. It can happen due to numerous reasons like poor management or scarce resources. Business intelligence vs Data Science Data science is commonly mistaken with business intelligence. Business intelligence focuses on analyzing the previous data and run research on it to explain the business trends. It manages, arranges and produces information from the data to answer business problems. It is much simpler than data science. Data science uses complex tools and statistics and analyzes data based on past or current to forecast the future trends. it answers open ended questions as to how and what could happen in the future while BI focuses on the question that asks what happened only. BI has a limited scope as it focuses on past and present, data science focuses on present and future and has unlimited scope. BI contains data that is only structured, while data science contains both structured and unstructured data. BI helps companies in solving their problems while data scientists raise the problems and solve them too. Tools that are used in BI are MS excel, SAS BI, MicroStrategy. Tools used by data science are Hadoop, Qlikview, Python, TensorFlow. Artificial intelligence vs Data science Data science makes use of artificial intelligence but they are not entirely the same. Artificial intelligence is known as to counterfeit human intelligence into machines to make them capable of imitating humans and be able to solve problems and make decisions. Data science on the other hand is the process of analyzing, pre-processing and maintaining data for analytics and visualization to forecast future trends and patterns. Data science uses statistical techniques whereas artificial intelligence makes use of algorithms. Data science does not involve scientific processing as much as artificial intelligence. Data science uses data analytics technique while artificial intelligence uses machine learning. Big data vs Data Science These two terms are often heard together but they are quite distinguished from one another. Big data is focused on handling large data while Data science focuses on analyzing the data and predicting future outcomes. Big data includes the process of handling large volumes of data and generating insights while data science predicts the outcomes and analyzes the trends and makes rational and smart decisions accordingly. E-commerce, telecommunication and security service industries use big data. While data science plays a huge role in industries involving science, risk analytics, advertisements etc. Data scientist Being a data scientist holds great responsibility, proficiency and knowledge in the domain. A data scientist requires to be adept in statistics and mathematics to be able to analyze and process data properly. A data scientist is supposed to be good at machine learning. It is also important that the scientist has great understanding of the domain he is working in to be able to do he is work appropriately. He should be able to apply algorithms where requires and have good knowledge of coding skills. He should also have sufficient experience in working in the field. He should also be good at programming and CS fundamentals. A scientist should also have apt communication skills, as he will be directly in contact with the upper management and will have to communicate his results to them. Its important for a data scientist to understand his projects and fulfill them properly by asking the right questions, using the right resources and tools and then brief the entire process to the stakeholders appropriately to achieve accurate results. Pay scale of a data scientist According to glassdoor average salary of a data scientist is $113,309 per year. The pay varies from $83,000 as lowest to $154,000. As for the additional cash compensation, it ranges from $3,850 - $26,084, with an average of $11,258. However, the salary varies according to the industry as well. For example, Facebook pays its data scientist an average of $146,221 per year according to 115 salaries it pays and Microsoft data scientist makes an average of $129,435 per year as per the 79 salaries it gives. Conclusion Data science holds its pros and cons and it might take time for it to gain proficiency and momentum but it is without doubt an ever-evolving industry and holds the future. With the humungous outbreak of data, the need for data science will elevate and thus will provide more opportunities to make key businesses decisions. With this evolution, it is also important that data scientists stay motivated to perform their job sincerely and efficiently. | https://www.udemy.com/course/data-science-and-machine-learning-with-python-and-libraries-t/#instructor-1 | We're a Startup aiming to provide learning with breakthrough technology. Oue courses have real time coding experience , practical applications, real-world data, in-depth learning. We care for our students. We'll provide immediate answers to our student queries. Our courses include: Data Science Python Machine Learning Deep Learning Web Development Cybersecurity BlockChain Digital Marketing Much more! | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Data Engineer/Data Scientist - Power BI/ Python/ ETL/SSIS | Hands-on Data Interaction and Manipulation. | 3.8 | 291 | 33587 | Created by Bluelime Learning Solutions | Jun-21 | English | $9.99 | 13h 17m total length | https://www.udemy.com/course/data-engineerdata-scientist-power-bi-python-etlssis/ | Bluelime Learning Solutions | Learning made simple | 4.1 | 36082 | 742296 | A common problem that organizations face is how to gathering data from multiple sources, in multiple formats, and move it to one or more data stores. The destination may not be the same type of data store as the source, and often the format is different, or the data needs to be shaped or cleaned before loading it into its final destination. Extract, transform, and load (ETL) is a data pipeline used to collect data from various sources, transform the data according to business rules, and load it into a destination data store. SQL Server Integration Services (SSIS) is a useful and powerful Business Intelligence Tool . It is best suited to work with SQL Server Database . It is added to SQL Server Database when you install SQL Server Data Tools (SSDT)which adds the Business Intelligence Templates to Visual studio that is used to create Integration projects. SSIS can be used for: Data Integration Data Transformation Providing solutions to complex Business problems Updating data warehouses Cleaning data Mining data Managing SQL Server objects and data Extracting data from a variety of sources Loading data into one or several destinations Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports. Discover how to quickly glean insights from your data using Power BI. This formidable set of business analytics tools—which includes the Power BI service, Power BI Desktop, and Power BI Mobile—can help you more effectively create and share impactful visualizations with others in your organization. In this beginners course you will learn how to get started with this powerful toolset. We will cover topics like connecting to and transforming web based data sources. You will learn how to publish and share your reports and visuals on the Power BI service. Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information Data is a fundamental part of our everyday work, whether it be in the form of valuable insights about our customers, or information to guide product,policy or systems development. Big business, social media, finance and the public sector all rely on data scientists to analyse their data and draw out business-boosting insights. Python is a dynamic modern object -oriented programming language that is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer.It is also known as a general purpose programming language due to it's flexibility. Python is used a lot in data science. This course is a beginners course that will introduce you to some basics of data science using Python. What You Will Learn How to set up environment to explore using Jupyter Notebook How to import Python Libraries into your environment How to work with Tabular data How to explore a Pandas DataFrame How to explore a Pandas Series How to Manipulate a Pandas DataFrame How to clean data How to visualize data | https://www.udemy.com/course/data-engineerdata-scientist-power-bi-python-etlssis/#instructor-1 | Bluelime is UK based and creates quality easy to understand eLearning solutions .All our courses are 100% video based. We teach hands –on- examples that teach real life skills . Bluelime has engaged in various types of projects for fortune 500 companies and understands what is required to prepare students with the relevant skills they need. | Data Engineer | >=3 | Below 1K | >=30K | >=4 | Below 1 Lakh | >=5 Lakh | ||||||||||||||||||
Learn Streamlit Python | Create Beautiful Data Apps and Machine Learning Web Apps In Python Faster with Streamlit | Bestseller | 4.5 | 288 | 2289 | Created by Jesse E. Agbe | Oct-22 | English | $10.99 | 30h 10m total length | https://www.udemy.com/course/learn-streamlit-python/ | Jesse E. Agbe | Developer | 4.2 | 861 | 6755 | Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit - a python framework for building web apps. Welcome to the coolest online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the awesome Streamlit Framework and Python. This course will teach you Streamlit - the python framework that saves you from spending days and weeks in creating data science and machine learning web applications. In this course we will cover everything you need to know concerning streamlit such as Fundamentals and the Basics of Streamlit ; - Working with Text - Working with Widgets (Buttons,Sliders, - Displaying Data - Displaying Charts and Plots - Working with Media Files (Audio,Images,Video) - Streamlit Layouts - File Uploads - Streamlit Static Components Creating cool data visualization apps How to Build A Full Web Application with Streamlit By the end of this exciting course you will be able to Build data science apps in hours not days Productionized your machine learning models into web apps using streamlit Build some cools and fun data apps Deploy your streamlit apps using Docker,Heroku,Streamlit Share and more Join us as we explore the world of building Data and ML Apps. See you in the Course,Stay blessed. Tips for getting through the course Please write or code along with us do not just watch,this will enhance your understanding. You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you. Suggested Prerequisites is understanding of Python This course is about Streamlit an ML Framework to create data apps in hours not weeks. We will try our best to cover some concepts for the beginner and the pro . | https://www.udemy.com/course/learn-streamlit-python/#instructor-1 | Hi, I am Jesse, a developer and a researcher with an obsession about optimizing available technologies in the best way possible by building simple and useful tools and by teaching others how to do so. My goal is to help people to optimize and harness tech to solve certain kinds of problems as well as to grow in life,faith and business. Data changed my life, and I am looking forward to share how we can utilize data to change humanity. Join me as we learn and build together. | Python | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||
Linear Algebra Mathematics for Machine Learning Data Science | Go Zero to Pro - Complete linear algebra - Mathematics for data science, machine learning & Deep Learning | 4.6 | 286 | 4067 | Created by Manifold AI Learning ® | Nov-22 | English | $11.99 | 20h 57m total length | https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-ai/ | Manifold AI Learning ® | Learn the Future - Data Science, Machine Learning & AI | 4.6 | 567 | 18684 | Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills - Most Importantly Linear Algebra? Then, this course is for you. With the growing learners of Machine Learning, Data Science, and Deep Learning. The Common mistake by a data scientist is→ Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithm work, its limitations and its underlying assumptions. With this, you will have an edge over your peers and makes you more confident in all the applications of Machine Learning, Data Science, and Deep Learning. As a common saying: It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car. Linear Algebra is one of the areas where everyone agrees to be a starting point in the learning curve of Machine Learning, Data Science, and Deep Learning.. Its basic elements – Vectors and Matrices are where we store our data for input as well as output. Any operation or Processing involving storing and processing the huge number of data in Machine Learning, Data Science, and Artificial intelligence, would mostly use Linear Algebra in the backend. Even Deep Learning and Neural Networks - Employs the Matrices to store the inputs like image, text etc. to give the state of the art solution to complex problems. Keeping in mind the significance of Linear Algebra in a Data Science career, we have tailor-made this curriculum such that you will be able to build a strong intuition on the concepts in Linear Algebra without being lost inside the complex mathematics. At the end of this course, you will also learn, how the Famous Google PageRank Algorithm works, using the concepts of Linear Algebra which we will be learning in this course. In this course. you will not only learn analytically, but you will also see its working by running in Python as well. So, with this course, you will learn, build intuition, and apply to some of the interesting real-world applications. Click on the Enroll Button to start Learning. I look forward to seeing you in Lecture 1 Course Contents: In this course you will take a step by step journey in mastering the Linear Algebra that you would require for Data Science, Machine Learning , Natural Language Processing and Deep Learning. Below lists down the content, and keep in mind - its a hands-on course. Vectors Basics : Vector Projections: Basis of Vectors Matrices Basics Matrix Transformations Gaussian Elimination Einstein Summation Convention Eigen Problems Google Page Rank Algorithm SVD - Singular Value Decomposition Pseudo Inverse Matrix Decomposition Solve Linear Regression using Matrix Methods Linear Regression from Scratch Linear Algebra in Natural Language Processing Linear Algebra for Deep Learning Linear Regression using PyTorch Bonus (Python Basics & Python for Data Science) | https://www.udemy.com/course/linear-algebra-for-data-science-machine-learning-ai/#instructor-1 | Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence. Checkout our instructor profile for the complete list of courses. All the best for your Learning. - Team ManifoldAILearning ® "Learn the Future" | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Computer Vision: Face Recognition Quick Starter in Python | Python Deep Learning based Face Detection, Recognition, Emotion , Gender and Age Classification using all popular models | Bestseller | 4.6 | 284 | 5095 | Created by Abhilash Nelson | Nov-22 | English | $11.99 | 9h 21m total length | https://www.udemy.com/course/computer-vision-face-recognition-quick-starter-in-python/ | Abhilash Nelson | Computer Engineering Master & Senior Programmer at Dubai | 4.2 | 2186 | 41457 | Hi There! welcome to my new course 'Face Recognition with Deep Learning using Python'. This is an updated course from my Computer Vision series which covers Python Deep Learning based Face Detection, Face Recognition, Emotion , Gender and Age Classification using all popular models including Haar Cascade, HOG, SSD, MMOD, MTCNN, EigenFace, FisherFace, VGGFace, FaceNet, OpenFace, DeepFace Face Detection and Face Recognition is the most used applications of Computer Vision. Using these techniques, the computer will be able to extract one or more faces in an image or video and then compare it with the existing data to identify the people in that image. Face Detection and Face Recognition is widely used by governments and organizations for surveillance and policing. We are also making use of it daily in many applications like face unlocking of cell phones etc. This course will be a quick starter for people who wants to dive deep into face recognition using Python without having to deal with all the complexities and mathematics associated with typical Deep Learning process. We will be using a python library called face-recognition which uses simple classes and methods to get the face recognition implemented with ease. We are also using OpenCV, Dlib and Pillow for python as supporting libraries. Let's now see the list of interesting topics that are included in this course. At first we will have an introductory theory session about Face Detection and Face Recognition technology. After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package. Then we will install the rest of dependencies and libraries that we require including the dlib, face-recognition, opencv etc and will try a small program to see if everything is installed fine. Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures. Then we will have an introduction to the basics and working of face detectors which will detect human faces from a given media. We will try the python code to detect the faces from a given image and will extract the faces as separate images. Then we will go ahead with face detection from a video. We will be streaming the real-time live video from the computer's webcam and will try to detect faces from it. We will draw rectangle around each face detected in the live video. In the next session, we will customize the face detection program to blur the detected faces dynamically from the webcam video stream. After that we will try facial expression recognition using pre-trained deep learning model and will identify the facial emotions from the real-time webcam video as well as static images And then we will try Age and Gender Prediction using pre-trained deep learning model and will identify the Age and Gender from the real-time webcam video as well as static images After face detection, we will have an introduction to the basics and working of face recognition which will identify the faces already detected. In the next session, We will try the python code to identify the names of people and their the faces from a given image and will draw a rectangle around the face with their names on it. Then, like as we did in face detection we will go ahead with face recognition from a video. We will be streaming the real-time live video from the computer's webcam and will try to identify and name the faces in it. We will draw rectangle around each face detected and beneath that their names in the live video. Most times during coding, along with the face matching decision, we may need to know how much matching the face is. For that we will get a parameter called face distance which is the magnitude of matching of two faces. We will later convert this face distance value to face matching percentage using simple mathematics. In the coming two sessions, we will learn how to tweak the face landmark points used for face detection. We will draw line joining these face land mark points so that we can visualize the points in the face which the computer is used for evaluation. Taking the landmark points customization to the next level, we will use the landmark points to create a custom face make-up for the face image. Till now we were using the face-recognition third party library to achieve most of the functionality. But from now onwards we will try the face-recognition pipeline steps which includes face detection, face alignment, face feature extraction verification and classification separately one by one using popular libraries. We will have an introduction about these in this session. In the next session, we will start with face detection. We will divide them into traditional face detection methods and modern methods which involves CNN. At first we will try the Haar Cascade object detection algorithm for face detection. We will try it at first for still images and later we will implement it for saved videos as well as live web cam videos. Another popular algorithm for face detection is HOG or Histogram of Oriented Gradients. At first we will have an introduction to the working of HOG algorithm and then we will try the HOG method for images, videos and real-time web cam stream. The next face detection algorithm we will try is SSD or Single Shot Detection. We will repeat the same functionality exercises for SSD also. And then comes MMOD. We will repeat the same functionality exercises for SSD also. Then the MMOD, the Max-Margin Object Detection. We will repeat the same functionality exercises for MTCNN also. Then comes the next algorithm which is MTCNN, the Multi-task Cascaded Convolutional Networks. We will repeat the same functionality exercises including image, video and real time stream for MTCNN also. Finally we will have a quick comparison between the performance of these face detection algorithms. After face detection, we will go ahead with face alignment. We will use the popular Dlib library python implementation to perform the face alignment for image, video and video streams. After face alignment exercises, we will proceed with face verification and classification where the actual face recognition is happening. At first we will have an introduction about face classification. We will divide the techniques into traditional face recognition methods and modern methods which involves CNN. At first we will try the techniques Eigenface Fisherface and LBPH, the Local Binary Pattern Histogram. We will have a short introduction about these algorithms and will then proceed with preparing the image dataset for these algorithms. Then we will set up the prerequisite for them. Later we will proceed with face detection using MTCNN and preprocessing of the detected face for recognition. Then the exercises involving training with the image dataset and trying prediction for images. We will then save this model so that we can load it later and do prediction without having to go through training again. We will also try it for pre-saved videos and real time webcam stream. Once we are done with that we will have a quick comparison of the Eigenface Fisherface and LBPH algorithms. That's all the traditional ways, now we will proceed with deep learning face recognition. At first using the popular VGGNet model for face recognition called VGGface. We will have an introduction to VGG face and then we will implement VGGface face verification for images. Later we will try VGGface face verification for videos as well as realtime streams. And then we have an introduction to FaceNet, OpenFace and DeepFace Models. We will use a popular easy to use open source python face recognition framework called deepface to implement the rest of popular deep learning techniques. We will install deepface to our computer and then try it at first for face detection and face alignment. Then we will try deepface for face one to one verification. Later with few changes, we can use it for face classification which involves an one to many comparison. deepface can also be used for performing face analysis involving gender, age, emotion, ethnicity etc That's all about the topics which are currently included in this quick course. The code, images and libraries used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio. So that's all for now, see you soon in the class room. Happy learning and have a great time. | https://www.udemy.com/course/computer-vision-face-recognition-quick-starter-in-python/#instructor-1 | I am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design. | Computer Vision | Senior Role | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Deploy Serverless Machine Learning Models to AWS Lambda | Use Serverless Framework for fast deployment of different ML models to scalable and cost-effective AWS Lambda service. | 4.6 | 279 | 2439 | Created by Milan Pavlović | Dec-20 | English | $12.99 | 7h 45m total length | https://www.udemy.com/course/deploy-serverless-machine-learning-models-to-aws-lambda/ | Milan Pavlović | Data Scientist | 4.6 | 279 | 2439 | In this course you will discover a very scalable, cost-effective and quick way of deploying various machine learning models to production by using principles of serverless computing. Once when you deploy your trained ML model to the cloud, the service provider (AWS in this course) will take care of managing server infrastructure, automated scaling, monitoring, security updating and logging. You will use free AWS resources which are enough for going through the entire course. If you spend them, which is very unlikely, you will pay only for what you use. By following course lectures, you will learn about Amazon Web Services, especially Lambda, API Gateway, S3, CloudWatch and others. You will be introduced with various real-life use cases which deploy different kinds of machine learning models, such as NLP, deep learning computer vision or regression models. We will use different ML frameworks - scikit-learn, spaCy, Keras / Tensorflow - and show how to prepare them for AWS Lambda. You will also be introduced with easy-to-use and effective Serverless Framework which makes Lambda creation and deployment very easy. Although this course doesn't focus much on techniques for training and fine-tuning machine learning models, there will be some examples of training the model in Jupyter Notebook and usage of pre-trained models. | https://www.udemy.com/course/deploy-serverless-machine-learning-models-to-aws-lambda/#instructor-1 | After finishing a bachelor degree in Information Systems, I graduated Information and Software Engineering master study at Faculty of Organization and Informatics, University of Zagreb, in 2016. During the study I was 100% of time in top 2% of students and received 5 Dean's awards in total (2011-2016) and 2 Summa Cum Laude honors. I worked as a teaching assistant for almost two years, after which I moved to industry. During my academic career, I collaborated with Text Analysis and Knowledge Engineering Lab at the Faculty of Electrical Engineering and Computing, University of Zagreb, where I also successfully completed Machine Learning, Deep Learning and Text Analysis and Retrieval master courses. Through this period I gained a solid understanding of machine learning, deep learning and natural language processing. Currently I work as a Data Scientist for one Croatian startup. My main fields of expertise are natural language processing (text semantics, classification and retrieval). I build AI-powered systems by creating and deploying machine learning models to production environments. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Image Recognition for Beginners using CNN in R Studio | Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio | 4.3 | 279 | 82438 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 6h 35m total length | https://www.udemy.com/course/cnn-for-computer-vision-with-keras-and-tensorflow-in-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create an Image Recognition model in R, right? You've found the right Convolutional Neural Networks course! After completing this course you will be able to: Identify the Image Recognition problems which can be solved using CNN Models. Create CNN models in R using Keras and Tensorflow libraries and analyze their results. Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course. If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in R without getting too Mathematical. Why should you choose this course? This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks. Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 300,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Practice test, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems. Below are the course contents of this course on ANN: Part 1 (Section 2)- Setting up R and R Studio with R crash course This part gets you started with R. This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Part 2 (Section 3-6) - ANN Theoretical Concepts This part will give you a solid understanding of concepts involved in Neural Networks. In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model. Part 3 (Section 7-11) - Creating ANN model in R In this part you will learn how to create ANN models in R. We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models. We also understand the importance of libraries such as Keras and TensorFlow in this part. Part 4 (Section 12) - CNN Theoretical Concepts In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models. In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model. Part 5 (Section 13-14) - Creating CNN model in R In this part you will learn how to create CNN models in R. We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part. Part 6 (Section 15-18) - End-to-End Image Recognition project in R In this section we build a complete image recognition project on colored images. We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition). By the end of this course, your confidence in creating a Convolutional Neural Network model in R will soar. You'll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below are some popular FAQs of students who want to start their Deep learning journey- Why use R for Deep Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/cnn-for-computer-vision-with-keras-and-tensorflow-in-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Image Recognition | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Python for Data Science | An introductory to intermediate level program in Python, and how to apply it in data science | 4.2 | 278 | 32385 | Created by Starweaver Team, Paul Siegel | Aug-22 | English | $9.99 | 9h 9m total length | https://www.udemy.com/course/top-python-for-data-science-course/ | Starweaver Team | Learning | Doing | Connecting® | 4.5 | 18714 | 197143 | This course is an foundational introduction to Python and how to apply it in data science. The course contains about 60 lectures and 7.5 hours of content taught by Praba Santanakrishnan, a highly experienced data scientist and former senior data science and AI expert veteran at Microsoft. Starting with some fundamentals about "what is data science," and "who is a data scientist," the program rapidly move into the specific challenges of data science. This includes the challenges of: Problem definitions and collecting data Data science methodologies Types of machine learning, including supervised and unsupervised machine learning, as well as methodologies and clustering Data pipelines Data preparation and cleaning Data analytics and open source tools Data model building validation Data visualization NumPy, Pandas, Python Notebook, Git, REPL, IDS and Jupyter Notebook. Arrays, advanced arrays, and matrices Other various data science applications More about this course and Starweaver This course is led by a seasoned technology industry practitioner and executive with many years of hands-on, in-the-trenches data science, artificial intelligence and data analytics work. It has been designed, produced and delivered by Starweaver. Starweaver is one of the most highly regarded, well-established training providers in the World, providing training courses to many of the leading financial institutions and technology companies, including: Ahli United Bank; Mashreqbank; American Express; ANZ Bank; ATT; Banco Votorantim; Bank of America; Bank of America Global Markets; Bank of America Private Bank; Barclay Bank; BMO Financial Group; BMO Financial Services; BNP Paribas; Boeing; Cigna; Citibank; Cognizant; Commerzbank; Credit Lyonnais/Calyon; Electrosonic; Farm Credit Administration; Fifth Third Bank; GENPACT; GEP Software; GLG Group; Hartford; HCL; HCL; Helaba; HSBC; HSBC Corporate Bank; HSBC India; HSBC Private Bank; Legal & General; National Australia Bank; Nomura Securities; PNC Financial Services Group; Quintiles; RAK Bank; Regions Bank; Royal Bank of Canada; Royal Bank of Scotland; Santander Corporate Bank; Tata Consultancy Services; Union Bank; ValueMomentum; Wells Fargo; Wells Fargo India Solutions; Westpac Corporate Bank; Wipro; and, many others. Starweaver has and continues to deliver 1000s of live in person and online education for organizational training programs for new hires and induction, as well as mid-career and senior level immersion and leadership courses. If you are looking for live streaming education or want to understand what courses might be best for you in technology or business, just google: starweaver journey builder starweaver[dot]com Happy learning. | https://www.udemy.com/course/top-python-for-data-science-course/#instructor-1 | We are on a mission to transform technologists into world-class experts, and business people into tech-savvy leaders. It's that simple! We are a technology and media company that delivers a completely new, immersive, addictive and connected education experience online for technology and business pros. We help you develop habits and skills that kick your talent into a star-level of performance! We deliver superlative, hands-on and practical professional development and education focused on several key domains: 1. Data Science, including big data, machine learning, data visualization, data analytics... 2. Cloud Computing, including Amazon Web Services, Microsoft Azure, Google Cloud Platform / Firebase, IoT, Platform as a Service, Infrastructure as a Services, Software as a Service... 3. Full Stack, including from front end (customer or user-facing) to the back end (the "behind-the-scenes" technology such as databases and internal architecture) including http, CSS, JavaScript, MongoDB, NodeJS, Angular, React... 4. Agility & Stability, including a broad group of frameworks and techniques involving Agile, Scrum, Kanban, TOGAF, ITIL, cybersecurity... 5. Business domains, including a strong emphasis on banking, finance, securities, and insurance (e.g., derivatives, credit risk management, corporate finance, financial modelling...)... Our business is designed to boost your knowledge and skills so that you can apply what you gain immediately in your job, or that next great job you are looking to start. We address, largely, more advanced, or complex subjects and convert those into rational, understandable, and usable information you can employ right away. If you want to take a great leap forward in your career, we are here to make that happen. Our instructors are seasoned practitioners who have worked in the fields their cover and relate the subjects covered to real work situations you will encounter in your job. So, the concepts are real, the coverage is at the right level of depth, and the material is straightforward. Who trusts us with their training requirements? The world's preeminent companies, organizations, and agencies. Specifically? Check out our website and you will see that these are the best of the best. | Python | >=4 | Below 1K | >=30K | >=4 | Below 1 Lakh | >=1.5 Lakh | ||||||||||||||||||
Natural Language Processing: Machine Learning NLP In Python | A Complete Beginner NLP Syllabus. Practicals: Linguistics, Flask,Sentiment, Scrape Tweets, Chatbot, Hugging Face & more! | 4.4 | 279 | 2297 | Created by Nidia Sahjara, Rajeev D. Ratan | Nov-21 | English | $9.99 | 19h 44m total length | https://www.udemy.com/course/nidia-natural-language-processing-deep-learning-zero-to-hero/ | Nidia Sahjara | NLP Engineer & Researcher | 4.3 | 2969 | 47675 | This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python - with very simple examples as you code along with me. Get experience doing a full real-world workflow from Collecting your own Data to NLP Sentiment Analysis using Big Datasets of over 50,000 Tweets. Data collection: Scrape Twitter using: OSINT - Open Source Intelligence Tools: Gather text data using real-world techniques. In the real world, in many instances you would have to create your own data set; i.e source your data instead of downloading a clean, ready-made file online Use Python to search relevant tweets for your study and NLP to analyze sentiment. Language Syntax: Most NLP courses ignore the core domain of Linguistics. This course explains the fundamentals of Language Syntax & Parse trees - the foundation of how a machine can interpret the structure of s sentence. New to Python: If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line. No Installs, we go straight to coding - Code using Google Colab - to be up-to-date with what's being used in the Data Science world 2021! The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language-based, Non-Mathematical) theories of Deep Learning. Natural Language Processing Foundation Linguistics & Semantics - study the background theory on natural language to better understand the Computer Science applications Pre-processing Data (cleaning) Regex, Tokenization, Stemming, Lemmatization Name Entity Recognition (NER) Part-of-Speech Tagging SQuAD SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset. Libraries: NLTK Sci-kit Learn Hugging Face Tensorflow Pytorch SpaCy Twint The topics outlined below are taught using practical Python projects! Parse Tree Markov Chain Text Classification & Sentiment Analysis Company Name Generator Unsupervised Sentiment Analysis Topic Modelling Word Embedding with Deep Learning Models Closed Domain Question Answering (Like asking questions on many different topics, from Beyonce to Iranian Cuisine) LSTM using TensorFlow, Keras Sequence Model Speech Recognition Convert Speech to Text Neural Networks This is taught from first principles - comparing Biological Neurons in the Human Brain to Artificial Neurons. Practical project: Sentiment Analysis of Steam Reviews Word Embedding: This topic is covered in detail, similar to an undergraduate course structure that includes the theory & practical examples of: TF-IDF Word2Vec One Hot Encoding gloVe Deep Learning Recurrent Neural Networks LSTMs Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning. Build models using LSTMs | https://www.udemy.com/course/nidia-natural-language-processing-deep-learning-zero-to-hero/#instructor-1 | Nidia's specialities lie in war & conflict, data science and intelligence. She is a King's College Graduate and has a diverse background as her undergraduate studies include Computer Science and Civil & Environmental Engineering. She continued her postgraduate in Intelligence & Security. Her current research involves using NLP to analyse open-source data and opinion mining solutions. She is also a member of SHOC - the Strategic Hub for Organised Crime Research, as part of RUSI. | NLP | Researcher | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Deployment of Machine Learning Models in Production | Python | Deploy ML Model with BERT, DistilBERT, FastText NLP Models in Production with Flask, uWSGI, and NGINX at AWS EC2 | 4.1 | 277 | 7021 | Created by Laxmi Kant | Nov-22 | English | $9.99 | 9h 39m total length | https://www.udemy.com/course/nlp-with-bert-in-python/ | Laxmi Kant | Principal Data Scientist at mBreath and KGPTalkie | 4.4 | 1946 | 46559 | Are you ready to kickstart your Advanced NLP course? Are you ready to deploy your machine learning models in production at AWS? You will learn each and every steps on how to build and deploy your ML model on a robust and secure server at AWS. Prior knowledge of python and Data Science is assumed. If you are AN absolute beginner in Data Science, please do not take this course. This course is made for medium or advanced level of Data Scientist. You should have an introductory knowledge of Python, Machine Learning, and Natural Language Processing before enrolling in this course otherwise please do not enroll in this course. This is an advanced NLP course. What is BERT? BERT is a method of pre-training language representations, meaning that we train a general-purpose "language understanding" model on a large text corpus (like Wikipedia), and then use that model for downstream NLP tasks that we care about (like question answering). BERT outperforms previous methods because it is the first unsupervised, deeply bidirectional system for pre-training NLP. Unsupervised means that BERT was trained using only a plain text corpus, which is important because an enormous amount of plain text data is publicly available on the web in many languages. Why is BERT so revolutionary? Not only is it a framework that has been pre-trained with the biggest data set ever used, but it is also remarkably easy to adapt to different NLP applications, by adding additional output layers. This allows users to create sophisticated and precise models to carry out a wide variety of NLP tasks. Here is what you will learn in this course Notebook Setup and What is BERT. Data Preprocessing. BERT Model Building and Training. BERT Model Evaluation and Saving. DistilBERT Model Fine Tuning and Deployment Deploy Your ML Model at AWS with Flask Server Deploy Your Model at Both Windows and Ubuntu Machine And so much more! All these things will be done on Google Colab which means it doesn't matter what processor and computer you have. It is super easy to use and plus point is that you have Free GPU to use in your notebook. | https://www.udemy.com/course/nlp-with-bert-in-python/#instructor-1 | I am a Principal Data Scientist at SleepDoc and a Ph.D. in Data Science from the Indian Institute of Technology (IIT). I had also co-founded a company, mBreath Technologies. I have 8+ years of experience in data science, team management, business development, and customer profiling. I have worked with startups and MNC. I have also taught programming at IIT for few years and then later started a YouTube channel, KGP Talkie with 20K+ subscribers. I am very well connected with industry and academia. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Getting Started with Data Management | Learn the basics of how organizations deal with data. Analyze data using Python and SQL. Explore Hadoop and Hive. | 4.3 | 276 | 1389 | Created by Philip Agaba | Nov-21 | English | $9.99 | 2h 15m total length | https://www.udemy.com/course/getting-started-with-data-management/ | Philip Agaba | Backend expert, Author | 4.4 | 371 | 2203 | Data Management is one of the most important competencies your company has. With Digital Transformation at the top of the strategic agenda for many large organizations, Data Governance and Data Management are vital to building a strong foundation for integration, analysis, execution, and overall business value. Business and data professionals are currently facing The Fourth Industrial Revolution's convergence of megatrends around Customer 360, Artificial Intelligence, Big Data, programmatic marketing, and globalization. To survive these unrelenting business pressures, it's more critical, and strategic, than ever to put your data to work! In this course, you will learn about the various disciplines of data management. First, you will discover what Data Governance is and why you might want to implement a governance program for your organization, after which you will go through some very basic exploratory Data Analysis using the Python programming language. Next up, you'll cover basic Database Design, Data Quality essentials, and the fundamentals of the Structured Query Language. Then, you will get hands-on with some rudimentary Data Integration ETL, as well as Big Data with Hadoop. Finally, you will explore the various disciplines in the Data Management space. By the end of the course, you will have a firm understanding of enterprise data management and what the various disciplines do. | https://www.udemy.com/course/getting-started-with-data-management/#instructor-1 | I currently maintain Linux Servers on-premise, and in the cloud. I manage multi-gigabyte Oracle Databases. I also enjoy writing applications in Ruby on Rails. I've been a certified Java developer since 2008 and I'm considered a Puppet expert. Nothing makes me happier than helping others succeed. | Misc | Author | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Generative A.I., from GANs to CLIP, with Python and Pytorch | Learn to code the most creative and exciting A.I. architectures, generative networks, from basic to advanced and beyond | 4.6 | 268 | 15663 | Created by Javier Ideami | Jul-21 | English | $9.99 | 8h 0m total length | https://www.udemy.com/course/generative-creative-ai-from-gans-to-clip-with-python-and-pytorch/ | Javier Ideami | Multidisciplinary engineer, researcher & creative director | 4.4 | 310 | 17026 | Generative A.I. is the present and future of A.I. and deep learning, and it will touch every part of our lives. It is the part of A.I that is closer to our unique human capability of creating, imagining and inventing. By doing this course, you gain advanced knowledge and practical experience in the most promising part of A.I., deep learning, data science and advanced technology. The course takes you on a fascinating journey in which you learn gradually, step by step, as we code together a range of generative architectures, from basic to advanced, until we reach multimodal A.I, where text and images are connected in incredible ways to produce amazing results. At the beginning of each section, I explain the key concepts in great depth and then we code together, you and me, line by line, understanding everything, conquering together the challenge of building the most promising A.I architectures of today and tomorrow. After you complete the course, you will have a deep understanding of both the key concepts and the fine details of the coding process. What a time to be alive! We are able to code and understand architectures that bring us home, home to our own human nature, capable of creating and imagining. Together, we will make it happen. Let's do it! | https://www.udemy.com/course/generative-creative-ai-from-gans-to-clip-with-python-and-pytorch/#instructor-1 | Javier Ideami is an expert in A.I and deep learning, specialized in advanced visualization, computer vision and generative architectures. He is a multidisciplinary engineer, researcher, creative director, artist and entrepreneur. Javier Ideami’s projects and talks have taken him from Silicon Valley to the jungles of Bali, including Stanford University and UC Berkeley, the United Nations FAO HQ, the financial center of London, the International Cultural Diplomacy Conference in Berlin and many others. | PyTorch | Head/Director | >=4 | Below 1K | >=15K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Data Science with Python (beginner to expert) | Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics | 4.3 | 268 | 30576 | Created by Uplatz Training | Dec-20 | English | $10.99 | 44h 33m total length | https://www.udemy.com/course/data-science-with-python-certification-training/ | Uplatz Training | Fastest growing Global IT Training Provider | 3.7 | 12189 | 381859 | A warm welcome to the Data Science with Python course by Uplatz. Data Science with Python involves not only using Python language to clean, analyze and visualize data, but also applying Python programming skills to predict and identify trends useful for decision-making. Why Python for Data Science? Since data revolution has made data as the new oil for organizations, today's decisions are driven by multidisciplinary approach of using data, mathematical models, statistics, graphs, databases for various business needs such as forecasting weather, customer segmentation, studying protein structures in biology, designing a marketing campaign, opening a new store, and the like. The modern data-powered technology systems are driven by identifying, integrating, storing and analyzing data for useful business decisions. Scientific logic backed with data provides solid understanding of the business and its analysis. Hence there is a need for a programming language that can cater to all these diverse needs of data science, machine learning, data analysis & visualization, and that can be applied to practical scenarios with efficiency. Python is a programming language that perfectly fits the bill here and shines bright as one such language due to its immense power, rich libraries and built in features that make it easy to tackle the various facets of Data Science. This Data Science with Python course by Uplatz will take your journey from the fundamentals of Python to exploring simple and complex datasets and finally to predictive analysis & models development. In this Data Science using Python course, you will learn how to prepare data for analysis, perform complex statistical analyses, create meaningful data visualizations, predict future trends from data, develop machine learning & deep learning models, and more. The Python programming part of the course will gradually take you from scratch to advanced programming in Python. You'll be able to write your own Python scripts and perform basic hands-on data analysis. If you aspire to become a data scientist and want to expand your horizons, then this is the perfect course for you. The primary goal of this course is to provide you a comprehensive learning framework to use Python for data science. In the Data Science with Python training you will gain new insights into your data and will learn to apply data science methods and techniques, along with acquiring analytics skills. With understanding of the basic python taught in the initial part of this course, you will move on to understand the data science concepts, and eventually will gain skills to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular Python toolkits such as pandas, NumPy, matplotlib, scikit-learn, and so on. The Data Science with Python training will help you learn and appreciate the fact that how this versatile language (Python) allows you to perform rich operations starting from import, cleansing, manipulation of data, to form a data lake or structured data sets, to finally visualize data - thus combining all integral skills for any aspiring data scientist, analyst, consultant, or researcher. In this Data Science using Python training, you will also work with real-world datasets and learn the statistical & machine learning techniques you need to train the decision trees and/or use natural language processing (NLP). Simply grow your Python skills, understand the concepts of data science, and begin your journey to becoming a top data scientist. Data Science with Python Programming - Course Syllabus 1. Introduction to Data Science Introduction to Data Science Python in Data Science Why is Data Science so Important? Application of Data Science What will you learn in this course? 2. Introduction to Python Programming What is Python Programming? History of Python Programming Features of Python Programming Application of Python Programming Setup of Python Programming Getting started with the first Python program 3. Variables and Data Types What is a variable? Declaration of variable Variable assignment Data types in Python Checking Data type Data types Conversion Python programs for Variables and Data types 4. Python Identifiers, Keywords, Reading Input, Output Formatting What is an Identifier? Keywords Reading Input Taking multiple inputs from user Output Formatting Python end parameter 5. Operators in Python Operators and types of operators - Arithmetic Operators - Relational Operators - Assignment Operators - Logical Operators - Membership Operators - Identity Operators - Bitwise Operators Python programs for all types of operators 6. Decision Making Introduction to Decision making Types of decision making statements Introduction, syntax, flowchart and programs for - if statement - if…else statement - nested if elif statement 7. Loops Introduction to Loops Types of loops - for loop - while loop - nested loop Loop Control Statements Break, continue and pass statement Python programs for all types of loops 8. Lists Python Lists Accessing Values in Lists Updating Lists Deleting List Elements Basic List Operations Built-in List Functions and Methods for list 9. Tuples and Dictionary Python Tuple Accessing, Deleting Tuple Elements Basic Tuples Operations Built-in Tuple Functions & methods Difference between List and Tuple Python Dictionary Accessing, Updating, Deleting Dictionary Elements Built-in Functions and Methods for Dictionary 10. Functions and Modules What is a Function? Defining a Function and Calling a Function Ways to write a function Types of functions Anonymous Functions Recursive function What is a module? Creating a module import Statement Locating modules 11. Working with Files Opening and Closing Files The open Function The file Object Attributes The close() Method Reading and Writing Files More Operations on Files 12. Regular Expression What is a Regular Expression? Metacharacters match() function search() function re match() vs re search() findall() function split() function sub() function 13. Introduction to Python Data Science Libraries Data Science Libraries Libraries for Data Processing and Modeling - Pandas - Numpy - SciPy - Scikit-learn Libraries for Data Visualization - Matplotlib - Seaborn - Plotly 14. Components of Python Ecosystem Components of Python Ecosystem Using Pre-packaged Python Distribution: Anaconda Jupyter Notebook 15. Analysing Data using Numpy and Pandas Analysing Data using Numpy & Pandas What is numpy? Why use numpy? Installation of numpy Examples of numpy What is ‘pandas’? Key features of pandas Python Pandas - Environment Setup Pandas – Data Structure with example Data Analysis using Pandas 16. Data Visualisation with Matplotlib Data Visualisation with Matplotlib - What is Data Visualisation? - Introduction to Matplotlib - Installation of Matplotlib Types of data visualization charts/plots - Line chart, Scatter plot - Bar chart, Histogram - Area Plot, Pie chart - Boxplot, Contour plot 17. Three-Dimensional Plotting with Matplotlib Three-Dimensional Plotting with Matplotlib - 3D Line Plot - 3D Scatter Plot - 3D Contour Plot - 3D Surface Plot 18. Data Visualisation with Seaborn Introduction to seaborn Seaborn Functionalities Installing seaborn Different categories of plot in Seaborn Exploring Seaborn Plots 19. Introduction to Statistical Analysis What is Statistical Analysis? Introduction to Math and Statistics for Data Science Terminologies in Statistics – Statistics for Data Science Categories in Statistics Correlation Mean, Median, and Mode Quartile 20. Data Science Methodology (Part-1) Module 1: From Problem to Approach Business Understanding Analytic Approach Module 2: From Requirements to Collection Data Requirements Data Collection Module 3: From Understanding to Preparation Data Understanding Data Preparation 21. Data Science Methodology (Part-2) Module 4: From Modeling to Evaluation Modeling Evaluation Module 5: From Deployment to Feedback Deployment Feedback Summary 22. Introduction to Machine Learning and its Types What is a Machine Learning? Need for Machine Learning Application of Machine Learning Types of Machine Learning - Supervised learning - Unsupervised learning - Reinforcement learning 23. Regression Analysis Regression Analysis Linear Regression Implementing Linear Regression Multiple Linear Regression Implementing Multiple Linear Regression Polynomial Regression Implementing Polynomial Regression 24. Classification What is Classification? Classification algorithms Logistic Regression Implementing Logistic Regression Decision Tree Implementing Decision Tree Support Vector Machine (SVM) Implementing SVM 25. Clustering What is Clustering? Clustering Algorithms K-Means Clustering How does K-Means Clustering work? Implementing K-Means Clustering Hierarchical Clustering Agglomerative Hierarchical clustering How does Agglomerative Hierarchical clustering Work? Divisive Hierarchical Clustering Implementation of Agglomerative Hierarchical Clustering 26. Association Rule Learning Association Rule Learning Apriori algorithm Working of Apriori algorithm Implementation of Apriori algorithm | https://www.udemy.com/course/data-science-with-python-certification-training/#instructor-1 | Uplatz is UK-based leading IT Training provider serving students across the globe. Our uniqueness comes from the fact that we provide online training courses at a fraction of the average cost of these courses in the market. Over a short span of 3 years, Uplatz has grown massively to become a truly global IT training provider with a wide range of career-oriented courses on cutting-edge technologies and software programming. Our specialization includes Data Science, Data Engineering, SAP, Oracle, Salesforce, AWS, Microsoft Azure, Google Cloud, IBM Cloud, SAS, Python, R, JavaScript, Java, Full Stack Web Development, Mobile App Development, BI & Visualization, Tableau, Power BI, Spotfire, Data warehousing, ETL tools, Informatica, IBM Data Stage, Digital Marketing, Agile, DevOps, and more. Founded in March 2017, Uplatz has seen phenomenal rise in the training industry starting with an online course on SAP FICO and now providing training on 5000+ courses across 103 countries having served 300,000 students in a period of just 3 years. Uplatz's training courses are highly structured, subject-focused, and job-oriented with strong emphasis on practice and assignments. Our courses are designed and taught by more than a thousand highly skilled and experienced tutors who have strong expertise in their areas whether it be AWS, Azure, Adobe, SAP, Oracle, or any other technology or in-demand software. | Python | >=4 | Below 1K | >=30K | >=3 | Below 1 Lakh | >=3.5 Lakh | ||||||||||||||||||
Artificial Intelligence Music Creation (2022 New Edition) | Learn the most amazing Artificial Intelligence (AI) Powered Music Creation Tools | 3.9 | 267 | 47046 | Created by Srinidhi Ranganathan | Oct-22 | English | $9.99 | 1h 5m total length | https://www.udemy.com/course/artificial-intelligence-music-creation-remixing-2018/ | Srinidhi Ranganathan | Digital Marketing Consultant | 3.9 | 24186 | 907599 | Welcome to experience "Artificial Intelligence Music Creation (2022 New Edition)". Giant Tech firms have developed AI software that can compose music on its own. So, the machines will be composing soundtracks using Artificial Intelligence. This game-changing course focuses on "Artificial Intelligence Music Creation (2022 New Edition)" introduces you to new-age technologies in Artificial Intelligence music creation to help you become a music star in no time with the power of automagical music-making tools. Why learn this music course and how is this a differentiator for music creators in 2022? This course can change your life if you are a music composer. Because, we will tell you the most popular Artificial Intelligence Music Creation tools that can help you compose music tracks without you - having any music knowledge whatsoever. We will also detail the latest discovery tools in Music Mashups and also we will go through the complete tutorial of Adobe Amper and Jukedeck - great AI music assistants. What’s more? Welcome to the future as you will be able to know the chords behind every song and just hum to create music like popular artists. I meant - You can just hum to create songs in the style of the great artists. The question is "Are you ready to get into action and embrace the power to leverage artificial intelligence in music creation in 2022?”. If yes, plunge into action right away by signing up NOW. There's no time to waste. A mind-blowing experience is in store for you at this moment. | https://www.udemy.com/course/artificial-intelligence-music-creation-remixing-2018/#instructor-1 | Important Note: Feel free to connect with Srinidhi on LinkedIn anytime. Catch some secretive educational videos created by Srinidhi on YouTube to further help in your learning by clicking the button on the right. About Srinidhi Ranganathan: Digital Marketing Consultant and Marketing Legend "Srinidhi Ranganathan" is the Chief Executive Officer (CEO) and Managing Director of First Look Digital Marketing Solutions (India's First Artificial Intelligence Powered Digital Marketing company) located in Bangalore and is one of the top instructors in India who is teaching highly futuristic digital marketing-related courses on Udemy. He is a Technologist, Digital Marketing Coach, Author, and Video Creation Specialist with over 10+ years of AIDM experience and has worked at top companies in India. Using his innovative marketing expertise, Srinidhi provides consulting services to startups and established brands utilising strategic planning and an extensive marketing audit powered by AI. He deploys the most comprehensive digital marketing strategy to clients worldwide that takes into account KPIs, methodology, and research statistics (utilising competitive intelligence software). Creating a growth hacking plan, content strategy, marketing mix, target segmentation analysis, competitor case-studies, brand strategy, local and global market research are also part of the consulting process to speed up a typical company's growth. Digital Marketing Legend "Srinidhi Ranganathan" has also helped startups and companies to leverage the best digital marketing strategies powered by automation to multi-fold their revenues. Having over 900,000+ students on Udemy - he has facilitated digital marketing analysis and provided state-of-the-art marketing strategy ideas and tactical execution plans for top marketing companies in India including startups, SMB's and MNC's. This includes strategic brainstorming sessions, Artificial Intelligence-powered market analysis, market research related to digital performance, support of various AIDM marketing initiatives for new product and consumer promotional launches, etc. He uses real-time forecasting, predictive modelling, machine learning, advanced machine learning-based optimisation techniques for business, marketing, Artificial Intelligence (AI) driven customer engagement strategies, competition monitoring software and other world-class tools. Srinidhi gained popularity through the unique, practical yet engaging training methodologies he utilises to teach during the training sessions. Some of his training methods include gamified learning experiences conducted by virtual writing and teaching robots like "Aera 2.0" that prompt behavioural changes in students and bring forth a new kind of fascination among the crowd. These robots are virtual humans having super-intelligence capabilities. They can autonomously train anyone on topics ranging from ABC to Rocket Science, without human intervention. Srinidhi's passionate fans call him a "Digital Marketing Legend" and he's busy working on creating new virtual and humanoid robots to revolutionise education in India and the world in 2022. He is deemed to be an innovator in the field of Artificial Intelligence (AI) based Digital Marketing and is someone who has embraced many ideas and has created various environments in which team members are taught the required AI automation tools and resources to challenge the status quo, push boundaries and achieve super-extensive growth. His courses are a testament to where the future is actually heading. "My goal has always been to give my students the AI tools to be able to leverage their digital marketing experiences, tools that allow them to build marketing success, from a whole new innovative perspective." — Srinidhi Ranganathan Srinidhi is currently working on these ultra-futuristic advancements in 2022 and creating research papers that contain information that delves 100-300 years into the future: 1) Autonomous Self-Thinking Scientific Computers 2) Personal Teleportation through Photons 3) Rise of Thought-to-Text and Dream Recognition Machines 4) Memory Regeneration with Nanobots 5) Birth of Hyper-Reality 6) Virtual Robo-Babies 7) Space-Tourism for the Wealthy 8) Mini-Flying Cities, Creation and Expansion using Maglev Technology 9) Self-Driving Flying Cars 10) Holographic Pets 11) Space-Probes (Stellar Light-Travelling Machines) 12) Virtual Digital Humans (Mind-Clones) 13) Computer-Like Lifeforms 14) Decentralised Artificial Intelligence (AI) Technologies 15) Mind-Revealing Technology 16) Cyborgs at the Workplace 17) Invisible Food 18) Temperature-Adaptable Smart Wearable Clothing 19) Holodeck-style Underwater Starship Homes 20) Yottabyte Storage Tech for Unimaginable Cloud Data Storage 21) Atmospheric Water Generation Secrets (2022-2050) | Artificial Intelligence | Consultant | >=3 | Below 1K | >=45K | >=3 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Cluster Analysis- Theory & workout using SAS and R | Unsupervised Machine Learning : Hierarchical & non hierarchical clustering (k-means), theory & SAS / R program | 4.3 | 264 | 1954 | Created by Gopal Prasad Malakar | Jul-22 | English | $9.99 | 6h 22m total length | https://www.udemy.com/course/cluster-analysis-motivation-theory-practical-application/ | Gopal Prasad Malakar | Trains Industry Practices on data science / machine learning | 4.2 | 10737 | 111703 | About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts. Course Structure (contents) The structure of the course is as follows. Part 01 - cluster analysis theory and workout using SAS ------------------------------ Motivation – Where one applies cluster analysis. Why one should learn cluster analysis? How it is different from objective segmentation (CHAID / CART ) Statistical foundation and practical application: Understand Different type of cluster analysis Cluster Analysis – high level view Hierarchical clustering – Agglomerative or Divisive technique Dendogram – What it is? What does it show? Scree plot - How to decide about number of clusters How to use SAS command to run hierarchical clustering When and why does on need to standardize the data? How to understand and interpret the output Non-hierarchical clustering (K means clustering). Why do we need k means approach How does it work? How does it iterate? How does it decide about combining old clusters? How to use SAS command to run hierarchical clustering When and why does on need to standardize the data? How to understand and interpret the output Part 02 --------------------- Learn R syntax for hierarchical and non hierarchical clustering Part 03 ------------------ Cluster analysis in data mining scenario Part 04 ---------------- Assignment on cluster analysis | https://www.udemy.com/course/cluster-analysis-motivation-theory-practical-application/#instructor-1 | I am a seasoned Analytics professional with 20+ years of professional experience. I have industry experience of impactful and actionable analytics, data science, decision strategy and enterprise wise data strategy. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting, business intelligence systems like tableau /SAS Visual Analytics, MS access based database application development, Enterprise wide big data framework and streaming analysis. Please refer to my course for - SAS / R program details (syntax and options) - SAS / R output deep dive - Practical usage in Industrial situation | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=1 Lakh | ||||||||||||||||||
RASA :Build and Deploy Chatbot On The Cloud (100% FREE) | Your RASA Course Guide From Installation to Deployment And Get Your RASA Certification ! | 3.6 | 263 | 33094 | Created by Yassine AI | Feb-21 | English | $11.99 | 1h 50m total length | https://www.udemy.com/course/create-artificial-intelligent-chatbot-with-rasa-in-one-hour/ | Yassine AI | Build and Deploy your chatbot using RASA | 3.6 | 263 | 33094 | This course will teach you how to build and deploy your chatbots - with the help of the open source framework RASA and the power of AI. What is special about this course: - Without Any Knowledge Requires , You Will Be Able To Build A Professional Chatbot Using Python, RASA And Deploy It Into The CLOUD (100% FREE) - All these technologies and languages will be explain for the absolutes beginners without any experience of development Why taking this course: This course is different from others by this structure: 1) Learn to deploy your Chatbot in 20 mins into your website ( creating of your website is integrated in this course ) 2) Understand the concept of each step for being able to create your new Chatbot 3) This course focus on the practical way to learn RASA ( with creating your own chatbot during your learning ) Why taking this course: This course is different from others by this structure: 1) Learn to deploy your Chatbot in 20 mins into your website ( creating of your website is integrated in this course ) 2) Understand the concept of each step for being able to create your new Chatbot 3) This course focus on the practical way to learn RASA ( with creating your own chatbot during your learning ) Why taking this course: This course is different from others by this structure: 1) Learn to deploy your Chatbot in 20 mins into your website ( creating of your website is integrated in this course ) 2) Understand the concept of each step for being able to create your new Chatbot 3) This course focus on the practical way to learn RASA ( with creating your own chatbot during your learning ) | https://www.udemy.com/course/create-artificial-intelligent-chatbot-with-rasa-in-one-hour/#instructor-1 | I'm a RASA developer with more than 4 years of experience. I'm specialist in deployment of RASA solutions in the cloud for FREE. My goal is to give my students the opportunity to explore all PAID Solutions for FREE. My students Can deploy any project into the cloud FOR 100% FREE. I will available for any question about RASA Framework. | Misc | >=3 | Below 1K | >=30K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Practical Introduction to Machine Learning with Python | Quickly Learn the Essentials of Artificial Intelligence (AI) and Machine Learning (ML) | 4.6 | 263 | 10582 | Created by Madhu Siddalingaiah | May-20 | English | $9.99 | 4h 17m total length | https://www.udemy.com/course/practical-machine-learning/ | Madhu Siddalingaiah | Technology Consultant | 4.4 | 866 | 40822 | LinkedIn released it's annual "Emerging Jobs" list, which ranks the fastest growing job categories. The top role is Artificial Intelligence Specialist, which is any role related to machine learning. Hiring for this role has grown 74% in the past few years! Machine learning is the technology behind self driving cars, smart speakers, recommendations, and sophisticated predictions. Machine learning is an exciting and rapidly growing field full of opportunities. In fact, most organizations can not find enough AI and ML talent today. If you want to learn what machine learning is and how it works, then this course is for you. This course is targeted at a broad audience at an introductory level. By the end of this course you will understand the benefits of machine learning, how it works, and what you need to do next. If you are a software developer interested in developing machine learning models from the ground up, then my second course, Practical Machine Learning by Example in Python might be a better fit. There are a number of machine learning examples demonstrated throughout the course. Code examples are available on github. You can run each examples using Google Colab. Colab is a free, cloud-based machine learning and data science platform that includes GPU support to reduce model training time. All you need is a modern web browser, there's no software installation is required! July 2019 course updates include lectures and examples of self-supervised learning. Self-supervised learning is an exciting technique where machines learn from data without the need for expensive human labels. It works by predicting what happens next or what's missing in a data set. Self-supervised learning is partly inspired by early childhood learning and yields impressive results. You will have an opportunity to experiment with self-supervised learning to fully understand how it works and the problems it can solve. August 2019 course updates include a step by step demo of how to load data into Google Colab using two different methods. Google Colab is a powerful machine learning environment with free GPU support. You can load your own data into Colab for training and testing. March 2020 course updates migrate all examples to Google Colab and Tensorflow 2. Tensorflow 2 is one of the most popular machine learning frameworks used today. No software installation is required. April/May 2020 course updates streamline content, include Jupyter notebook lectures and assignment. Jupyter notebook is the preferred environment for machine learning development. | https://www.udemy.com/course/practical-machine-learning/#instructor-1 | Madhu is a professional machine learning practitioner and data scientist. Madhu has three decades of interdisciplinary experience applying great technology for many different organizations, such as FINRA, Apple, Blue Cross/Blue Shield, Food & Drug Administration, and the US Department of Defense. Over the years, Madhu has developed numerous innovative products and solutions at start ups and established companies. Examples include: machine learning solutions, Internet of Things (IoT) devices, big data systems, mobile medical applications, as well as enterprise applications and specialized hardware for space science, 3D graphics, and wireless communications. Madhu has been awarded US and EU patents and has authored multiple books and training courses. Madhu has presented papers at technology conferences all over the world, including London, Munich, and Sydney, and many US locations. Madhu is also a private helicopter pilot and enjoys playing electric guitar. | Machine Learning | Consultant | >=4 | Below 1K | >=10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Clustering & Classification With Machine Learning In Python | Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In Python | 4.6 | 261 | 3829 | Created by Minerva Singh | Nov-22 | English | $11.99 | 6h 4m total length | https://www.udemy.com/course/clustering-classification-with-machine-learning-in-python/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | HERE IS WHY YOU SHOULD TAKE THIS COURSE: This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal.. By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge and boost your career to the next level. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University. I have several years of experience in analyzing real life data from different sources using data science techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing simple deep learning based models using Python THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING: • A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. • Machine Learning, Supervised Learning, Unsupervised Learning in Python • Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! With such a rigorous grounding in so many topics, you will be an unbeatable data scientist by the end of the course. NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable Python Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python. My course will help you implement the methods using real data obtained from different sources. After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python.. You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework! Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students! JOIN MY COURSE NOW! | https://www.udemy.com/course/clustering-classification-with-machine-learning-in-python/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Beginner's Guide to Python Data Analysis & Visualization | Get started on data science with pandas and numpy from scratch in Python 3. Learn thoroughly, with breeze. | 3.9 | 261 | 3439 | Created by Alan H Yue, Rake Smith | Dec-21 | English | $9.99 | 4h 1m total length | https://www.udemy.com/course/complete-pandas-bootcamp-with-python-3/ | Alan H Yue | Wealth & Insurance Analyst at HSBC, | 4 | 1381 | 14917 | Become a Data scientist! ==> Make astonishing graphics! This is the most comprehensive, yet straightforward, course for learning Python data science on Udemy! Whether you have never touched data science before, | https://www.udemy.com/course/complete-pandas-bootcamp-with-python-3/#instructor-1 | Alan is a professional Wealth & Insurance Analyst at HSBC Bank for 2 years. He is a SAS and Python master. He writes in SAS and Python in his everyday data analysis works. Before that, he worked at Reitmans Canada, Ltd. as a Pricing Analyst. He enjoys discovering the insights with data analysis. He first studied Marketing as his Bachelor's degree, then turned to pursue his master degree in Finance in Montreal. With his courses, Alan wants to make data analysis skills available for every millennial to learn - without the struggles and frustrations. On Udemy, over 6000+ students around the globe are learning SAS & Python data analysis skills from Alan. | Python | >=3 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Data Science and Machine Learning Masterclass with R | Data Science by IITan - Data Science :Data Manipulation , Data Science Data Visualization, Data Science : Data Analytics | 4.5 | 259 | 9428 | Created by Up Degree | May-19 | English | $199.99 | 15h 1m total length | https://www.udemy.com/course/business-analytics-with-r/ | Up Degree | New Skills Everyday! | 3.8 | 4671 | 84324 | Are you planing to build your career in Data Science in This Year? Do you the the Average Salary of a Data Scientist is $100,000/yr? Do you know over 10 Million+ New Job will be created for the Data Science Filed in Just Next 3 years?? If you are a Student / a Job Holder/ a Job Seeker then it is the Right time for you to go for Data Science! Do you Ever Wonder that Data Science is the "Most Hottest" Job Globally in 2018 - 2019! Above, we just give you a very few examples why you Should move into Data Science and Test the Hot Demanding Job Market Ever Created! The Good News is That From this Hands On Data Science and Machine Learning in R course You will Learn All the Knowledge what you need to be a MASTER in Data Science. Why Data Science is a MUST HAVE for Now A Days? The Answer Why Data Science is a Must have for Now a days will take a lot of time to explain. Let's have a look into the Company name who are using Data Science and Machine Learning. Then You will get the Idea How it BOOST your Salary if you have Depth Knowledge in Data Science & Machine Learning! Here we list a Very Few Companies : - Google - For Advertise Serving, Advertise Targeting, Self Driving Car, Super Computer, Google Home etc. Google use Data Science + ML + AI to Take Decision Apple: Apple Use Data Science in different places like: Siri, Face Detection etc Facebook: Data Science , Machine Learning and AI used in Graph Algorithm for Find a Friend, Photo Tagging, Advertising Targeting, Chatbot, Face Detection etc NASA: Use Data Science For different Purpose Microsoft: Amplifying human ingenuity with Data Science So From the List of the Companies you can Understand all Big Giant to Very Small Startups all are chessing Data Science and Artificial Intelligence and it the Opportunity for You! Why Choose This Data Science with R Course? We not only "How" to do it but also Cover "WHY" to do it? Theory explained by Hands On Example! 15+ Hours Long Data Science Course 100+ Study Materials on Each and Every Topic of Data Science! Code Templates are Ready to Download! Save a lot of Time What You Will Learn From The Data Science MASTERCLASS Course: Learn what is Data science and how Data Science is helping the modern world! What are the benefits of Data Science , Machine Learning and Artificial Intelligence Able to Solve Data Science Related Problem with the Help of R Programming Why R is a Must Have for Data Science , AI and Machine Learning! Right Guidance of the Path if You want to be a Data Scientist + Data Science Interview Preparation Guide How to switch career in Data Science? R Data Structure - Matrix, Array, Data Frame, Factor, List Work with R’s conditional statements, functions, and loops Systematically explore data in R Data Science Package: Dplyr , GGPlot 2 Index, slice, and Subset Data Get your data in and out of R - CSV, Excel, Database, Web, Text Data Data Science - Data Visualization : plot different types of data & draw insights like: Line Chart, Bar Plot, Pie Chart, Histogram, Density Plot, Box Plot, 3D Plot, Mosaic Plot Data Science - Data Manipulation - Apply function, mutate(), filter(), arrange (), summarise(), groupby(), date in R Statistics - A Must have for Data Sciecne Data Science - Hypothesis Testing | https://www.udemy.com/course/business-analytics-with-r/#instructor-1 | UpDegree is a Group of IT skilled People having sound technical knowledge on various IT domain. We work for different different MNC including Microsoft,IBM,CISCO,eBay,Amazon, Flipkart etc and a lot of Startups also. We teach you practical Hands on computer skills what you need for a Job in the IT Sector. Less theory and more practical! Learn through Example and Step by Step. We love to help you! | Machine Learning | >=4 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Artificial Intelligence and the Future of Work (2022) | Explore some mind-blowing artificial intelligence technologies that have the potential to shape the workforce | 4.3 | 258 | 49849 | Created by Srinidhi Ranganathan | Sep-22 | English | $9.99 | 1h 46m total length | https://www.udemy.com/course/the-rise-of-artificial-intelligence-at-work-in-2019-beyond/ | Srinidhi Ranganathan | Digital Marketing Consultant | 3.9 | 24186 | 907599 | Welcome to experience "Artificial Intelligence and the Future of Work (2022)". Did you know? A great technological shift is on the verge of occurring very soon. Disruptive Artificial Intelligence technologies are going to change the world and human labour will be replaced by robot workers and the shift has in-fact started. This mind-blowing course introduces you to the concept of Artificial Intelligence usage in the workplace along with providing you practical examples of the different platforms that deploy the same for automation. You will learn about the numerous Human Resources tools and usage of these in Artificial Intelligence, along with sales-based AI tools that can help you close the deal. You will be also introduced to Virtual chatbots that look like humans and do all the automation and support work for you in any industry you are in. We will also look at a particular case study of a company leveraging human robots as receptionists to free up tasks for the real employees. You will also learn about the AI-powered business advisor kind of tool that can provide valuable suggestions for analytics, for that matter. Ultimately, the rise of Artificial Intelligence at work in 2022 and beyond is not a dream. It is a continuous journey into the exploration of new-found technologies that are unknown to many which have the greatest power in them to change the world altogether. Enrol now and let's start booming. Don't keep waiting. The robots are coming for you, this year. Are there any course requirements or any kind of pre-requisites, before taking this course? None. However, several tools or AI platforms are taught in the course. You must go through these AI platforms completely and adopt any one of them based on your requirements or needs to skyrocket your business with AI. | https://www.udemy.com/course/the-rise-of-artificial-intelligence-at-work-in-2019-beyond/#instructor-1 | Important Note: Feel free to connect with Srinidhi on LinkedIn anytime. Catch some secretive educational videos created by Srinidhi on YouTube to further help in your learning by clicking the button on the right. About Srinidhi Ranganathan: Digital Marketing Consultant and Marketing Legend "Srinidhi Ranganathan" is the Chief Executive Officer (CEO) and Managing Director of First Look Digital Marketing Solutions (India's First Artificial Intelligence Powered Digital Marketing company) located in Bangalore and is one of the top instructors in India who is teaching highly futuristic digital marketing-related courses on Udemy. He is a Technologist, Digital Marketing Coach, Author, and Video Creation Specialist with over 10+ years of AIDM experience and has worked at top companies in India. Using his innovative marketing expertise, Srinidhi provides consulting services to startups and established brands utilising strategic planning and an extensive marketing audit powered by AI. He deploys the most comprehensive digital marketing strategy to clients worldwide that takes into account KPIs, methodology, and research statistics (utilising competitive intelligence software). Creating a growth hacking plan, content strategy, marketing mix, target segmentation analysis, competitor case-studies, brand strategy, local and global market research are also part of the consulting process to speed up a typical company's growth. Digital Marketing Legend "Srinidhi Ranganathan" has also helped startups and companies to leverage the best digital marketing strategies powered by automation to multi-fold their revenues. Having over 900,000+ students on Udemy - he has facilitated digital marketing analysis and provided state-of-the-art marketing strategy ideas and tactical execution plans for top marketing companies in India including startups, SMB's and MNC's. This includes strategic brainstorming sessions, Artificial Intelligence-powered market analysis, market research related to digital performance, support of various AIDM marketing initiatives for new product and consumer promotional launches, etc. He uses real-time forecasting, predictive modelling, machine learning, advanced machine learning-based optimisation techniques for business, marketing, Artificial Intelligence (AI) driven customer engagement strategies, competition monitoring software and other world-class tools. Srinidhi gained popularity through the unique, practical yet engaging training methodologies he utilises to teach during the training sessions. Some of his training methods include gamified learning experiences conducted by virtual writing and teaching robots like "Aera 2.0" that prompt behavioural changes in students and bring forth a new kind of fascination among the crowd. These robots are virtual humans having super-intelligence capabilities. They can autonomously train anyone on topics ranging from ABC to Rocket Science, without human intervention. Srinidhi's passionate fans call him a "Digital Marketing Legend" and he's busy working on creating new virtual and humanoid robots to revolutionise education in India and the world in 2022. He is deemed to be an innovator in the field of Artificial Intelligence (AI) based Digital Marketing and is someone who has embraced many ideas and has created various environments in which team members are taught the required AI automation tools and resources to challenge the status quo, push boundaries and achieve super-extensive growth. His courses are a testament to where the future is actually heading. "My goal has always been to give my students the AI tools to be able to leverage their digital marketing experiences, tools that allow them to build marketing success, from a whole new innovative perspective." — Srinidhi Ranganathan Srinidhi is currently working on these ultra-futuristic advancements in 2022 and creating research papers that contain information that delves 100-300 years into the future: 1) Autonomous Self-Thinking Scientific Computers 2) Personal Teleportation through Photons 3) Rise of Thought-to-Text and Dream Recognition Machines 4) Memory Regeneration with Nanobots 5) Birth of Hyper-Reality 6) Virtual Robo-Babies 7) Space-Tourism for the Wealthy 8) Mini-Flying Cities, Creation and Expansion using Maglev Technology 9) Self-Driving Flying Cars 10) Holographic Pets 11) Space-Probes (Stellar Light-Travelling Machines) 12) Virtual Digital Humans (Mind-Clones) 13) Computer-Like Lifeforms 14) Decentralised Artificial Intelligence (AI) Technologies 15) Mind-Revealing Technology 16) Cyborgs at the Workplace 17) Invisible Food 18) Temperature-Adaptable Smart Wearable Clothing 19) Holodeck-style Underwater Starship Homes 20) Yottabyte Storage Tech for Unimaginable Cloud Data Storage 21) Atmospheric Water Generation Secrets (2022-2050) | Artificial Intelligence | Consultant | >=4 | Below 1K | >=45K | >=3 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Machine Learning for Absolute Beginners - Level 2 | Learn the Python Fundamentals and Pandas Library for Data Science Projects | 4.5 | 257 | 21697 | Created by Idan Gabrieli | Sep-22 | English | $9.99 | 3h 59m total length | https://www.udemy.com/course/machine-learning-for-absolute-beginners-level-2/ | Idan Gabrieli | Online Teacher | Data, Cloud, AI | 4.5 | 6697 | 137748 | ********* Feedback from Students ************ Excellent course, allows you to go step by step and get to know the bookcase, direct to the point thank you for such good teaching Jesus David I am currently new in data science, I knew Python language and Competitive programming after that I was waiting to learn data science by myself. This course really very helpful for me to learn new things and data science and machine learning. I am really waiting for this new part (Level 3). Animesh K. A fantastic course for getting the knowledge of pandas in depth along with ML applications. This is what I am searching for. It had better if it has some more quiz questions to test our skills and understanding. Enjoyed a lot. Anshika Verma *********************************************** Unleash the Power of ML Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide! Machine Learning for Absolute Beginners The “Machine Learning for Absolute Beginners” training program is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. The training is divided into multiple levels, and each level is covering a group of related topics for continuous step-by-step learning. Level 2 - Python and Pandas The second course, as part of the training program, aims to help you start your practical journey. You will learn the Python fundamentals and the amazing Pandas data science library, including: Python syntax for developing data science projects Using JupyterLab tool for Jupiter notebooks Loading large datasets from files using Pandas Perform data analysis and exploration Perform data cleaning and transformation as a pre-processing step before moving into machine learning algorithms. Each section has a summary exercise as well as a complete solution to practice new knowledge. The Game just Started! Enroll in the training program and start your journey to become a data scientist! | https://www.udemy.com/course/machine-learning-for-absolute-beginners-level-2/#instructor-1 | For the past decade, Idan Gabrieli has been working in various engineering positions at the heart of Israel's high-tech industry, also called the start-up nation. Through his career, Idan has gained extensive experience working with hundreds of business companies, helping them transform challenges and opportunities into practical use cases while leveraging cutting-edge technologies. Idan has comprehensive knowledge that spans multiple domains, including cloud computing, machine learning, data science, electronics, and more. In 2014, Idan started to create and publish online courses on various topics while teaching thousands of students worldwide. In 2021-2022, Idan was recognized as a top-seller and high-rated instructor in multiple leading educational providers. As part of his teaching style, Idan is well-known for simplifying complex technology topics and providing high-quality educational content suitable to the relevant audience. Every course has specific learning objectives, easy-to-follow structure, and straight-to-point material while combining various multimedia teaching options. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=20K | >=4 | Below 10 K | >=1 Lakh | |||||||||||||||||
Machine Learning for Apps | Start building more intelligent apps with Machine Learning. Take advantage of this new foundational framework! | 4.4 | 255 | 11355 | Created by Devslopes by Mark Wahlbeck | Oct-17 | English | $9.99 | 6h 52m total length | https://www.udemy.com/course/machine-learning-for-apps/ | Devslopes by Mark Wahlbeck | Learn programming & app development | 4.3 | 54991 | 346974 | MACHINE LEARNING FOR APPS Welcome to the most comprehensive course on Core ML, one of Apples hot new features for iOS 11. The goal with Machine Learning is to mimic the human mind. It can be used to identify things like objects or images, make predictions and even analyze and identify speech. Dive in and learn the core concepts of machine learning and start building apps that can think! In this course you going to learn everything you need to know to start building more intelligent apps and your own ML Models. WHY TAKE THIS COURSE? Core ML is the first step if you want to start building apps with AI. Machine Learning opens an entirely new world to opportunities that will take your apps to the next level. Here are some of the things you'll be able to do after taking this course: Learn to code how the PROs code - not just copy and paste Build Real Projects - You'll get to build projects that help you retain what you've learned Build awesome apps that can make predictions Build amazing apps that can classify human handwriting WHAT YOU WILL LEARN: Learn about the foundation of Machine Learning and Core ML Learn foundational python Build a classification model allow your apps to make predictions Build a neural network for your app that can classify human writing Learn core ML concepts so you can build your own ML Model Utilize the power of Machine Learning and AI for use in iOS apps Learn how to pass in images to Apples pre trained model - MobileNet Don't forget to join the free live community where you can get free help anytime from other students | https://www.udemy.com/course/machine-learning-for-apps/#instructor-1 | Devslopes transforms beginner students into paid professionals through curated project based videos, interactive quizzes, and exercises. After completing each course, you will have a strong portfolio, coupled with the technical understanding to build your own custom applications. Our target students are: First time developers Entrepreneurs who want to build their own technology startup Current developers looking to either advance their careers or learn new technologies. By taking our courses, our students have been able to: Get jobs as developers with amazing salaries Launch (and even sell) their technology startups Get promotions and make substantial career changes We strive to teach students how to code through polished apps inspired by real world examples. We want our students to build projects that they are proud of, that look and operate just like apps they use in their everyday life. We are passionate about helping people reveal their hidden talents and guiding them into the exciting world of startups and programming. | Machine Learning | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | >=3 Lakh | ||||||||||||||||||
Linear Regression and Logistic Regression using R Studio | Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & Classification | 4.4 | 253 | 53038 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 6h 13m total length | https://www.udemy.com/course/linear-regression-and-logistic-regression-r-studio-starttech/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in R Studio, right? You've found the right Linear Regression course! After completing this course you will be able to: Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning. Create a linear regression and logistic regression model in R Studio and analyze its result. Confidently practice, discuss and understand Machine Learning concepts A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. How this course will help you? If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression Why should you choose this course? This course covers all the steps that one should take while solving a business problem through linear regression. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems. Below are the course contents of this course on Linear Regression: Section 1 - Basics of Statistics This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean median and mode and lastly measures of dispersion like range and standard deviation Section 2 - Python basic This section gets you started with Python. This section will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn. Section 3 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 4 - Data Preprocessing In this section you will learn what actions you need to take a step by step to get the data and then prepare it for the analysis these steps are very important. We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation. Section 5 - Regression Model This section starts with simple linear regression and then covers multiple linear regression. We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don't understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures. We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem. By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What is the Linear regression technique of Machine learning? Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value. Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x). When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, the method is known as multiple linear regression. Why learn Linear regression technique of Machine learning? There are four reasons to learn Linear regression technique of Machine learning: 1. Linear Regression is the most popular machine learning technique 2. Linear Regression has fairly good prediction accuracy 3. Linear Regression is simple to implement and easy to interpret 4. It gives you a firm base to start learning other advanced techniques of Machine Learning How much time does it take to learn Linear regression technique of machine learning? Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 4 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use Python for data Machine Learning? Understanding Python is one of the valuable skills needed for a career in Machine Learning. Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history: In 2016, it overtook R on Kaggle, the premier platform for data science competitions. In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools. In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals. Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/linear-regression-and-logistic-regression-r-studio-starttech/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Misc | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
neural networks for sentiment and stock price prediction | How to predict stock prices with neural networks and sentiment with neural networks. Machine learning hands on data scie | 4.5 | 253 | 1435 | Created by Dan We | Nov-22 | English | $11.99 | 2h 45m total length | https://www.udemy.com/course/neural-networks-for-stock-price-prediction-and-sentiment/ | Dan We | Coach | 4.5 | 13236 | 80477 | Let's dive into data science with python and predict stock prices and customer sentiment. machine learning / ai ? How to learn machine learning in python? And what is transfer learning ? How to use it ? How to create a sentiment classification algorithm in python? How to train a neural network for stock price prediction? Good questions here is a point to start searching for answers In the world of today and especially tomorrow machine learning and artificial intelligence will be the driving force of the economy. Data science No matter who you are, an entrepreneur or an employee, and in which industry you are working in, machine learning (especially deep learning neural networks) will be on your agenda. "From my personal experience I can tell you that companies will actively searching for you if you aquire some skills in the data science field. Diving into this topic can not only immensly improve your career opportunities but also your job satisfaction!" It's time to get your hands dirty and dive into one of the hottest topics on this planet. To me the best way to get exposure is to do it "Hands on". And that's exactly what we do. Together we will go through the whole process of data import, preprocess the data , creating an long short term neural network in keras (LSTM), training the neural network and test it (= make predictions) The course consists of 2 parts. In the first part we will create a neural network for stock price prediction. In the second part we create a neural network for sentiment analysis on twitter tweets. Let's get into it. See you in the first lecture | https://www.udemy.com/course/neural-networks-for-stock-price-prediction-and-sentiment/#instructor-1 | Dan is a 33 year old entrepreneur ,data enthusiast consultant and trainer. He holds a master degree and is certified in Power BI, Tableau, Alteryx (Core and Advanced) and KNIME (L1-L3). He is currently working in Business Intelligence field and helps companies and individuals to get key insights from their data to deliver long term growth and outpace their competitors. He has a passion for learning and teaching and is committed to support other people, by offering them educational services to help them accomplishing their goals and becoming the best in their profession or explore a new career path. "The dots will connect" Just do it! | Neural Networks | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||||
Introduction To Data Science | Use the R Programming Language to execute data science projects and become a data scientist. | 3.6 | 252 | 4580 | Created by Nina Zumel, John Mount | Mar-15 | English | $29.99 | 5h 52m total length | https://www.udemy.com/course/introduction-to-data-science/ | Nina Zumel | Data Scientist, Win-Vector LLC | 3.6 | 252 | 4580 | Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action. Understand Data Science to Be a More Effective Data Analyst ●Use R and RStudio ●Master Modeling and Machine Learning ●Load, Visualize, and Interpret Data Use R to Analyze Data and Come Up with Valuable Business Solutions This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go. You'll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems. All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders. Contents and Overview This course begins with a walk-through of a template data science project before diving into the R statistical programming language. You will be guided through modeling and machine learning. You'll use machine learning methods to create algorithms for a business, and you'll validate and evaluate models. You'll learn how to load data into R and learn how to interpret and visualize the data while dealing with variables and missing values. You’ll be taught how to come to sound conclusions about your data, despite some real-world challenges. By the end of this course, you'll be a better data analyst because you'll have an understanding of applied predictive modeling methods, and you'll know how to use existing machine learning methods in R. This will allow you to work with team members in a data science project, find problems, and come up solutions. You’ll complete this course with the confidence to correctly analyze data from a variety of sources, while sharing conclusions that will make a business more competitive and successful. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming. | https://www.udemy.com/course/introduction-to-data-science/#instructor-1 | Nina Zumel, PhD, has over 10 years of experience in research, machine learning, and data science. She is a co-author of the popular book Practical Data Science with R, co-author of the EMC data scientist certification program, and blogs often on statistics, data science, and data visualization. | Misc | Data Scientist | >=3 | Below 1K | Below 10K | >=3 | Below 1 K | Below 10 K | |||||||||||||||||
Statistical Arbitrage Bot Build in Crypto with Python (A-Z) | Build a Pairs Trade bot like a boss on the ByBit Crypto exchange with a statistical arbitrage edge in Python. | Bestseller | 4.5 | 251 | 2183 | Created by Shaun McDonogh | Mar-22 | English | $9.99 | 14h 18m total length | https://www.udemy.com/course/statistical-arbitrage-bot-build-in-crypto-with-python-a-z/ | Shaun McDonogh | Lead Analyst and Full Stack (Python and React) Developer | 4.6 | 1272 | 7748 | As requested by the Crypto Wizards community, this course provides you with: An intuitive understanding of trading principles in crypto (and other) markets Optimal calculations for risk, position sizing and entry/exit signals Everything you need to know to practically get started in Statistical Arbitrage How to find edge in multiple places and stack as many odds in your favour as possible Pairs trading concepts which can profit in upwards, sideways and downwards (all) market conditions An understand of Statistical Arbitrage and associated metrics An understand on how trading works on a Crypto Exchange How to tap into exchange price information at lightening speed via WebSockets and REST API Python code and walkthrough (line-by-line) for finding your own co-integrated statistical arbitrage trading pairs Python code and walkthrough (line-by-line) for developing your own trading bot Most retail traders never learn some of what you will come across here, either because those who understand the concepts have not taken the time to break this down so that anyone can follow, or because there is so much nonsense existing today that filtering through the noise can be challenging. In this course, we aim to break down barriers so that absolutely ANYONE can understand and tap into the advantages that these techniques can provide. The lecturer, Shaun McDonogh, himself admits that he is not a math wiz, nor needs to be. Once you understand these principles, you can apply them anywhere. We will be using the ByBit exchange (taking advantage of one major benefit offered by the exchange) to build and test our bot. At no point do we use real money for testing. Rather, we use the testate funds provided by the exchange for ensuring forward testing in a "live" testate environment. IMPORTANT: This course is for educational purposes only. Nothing you learn in this course is promising favourable or adverse results. You will be learning known methods for calculating statistical arbitrage and building trading bots. How you test and implement this knowledge is up to you. | https://www.udemy.com/course/statistical-arbitrage-bot-build-in-crypto-with-python-a-z/#instructor-1 | "I care about having an idea that is unusual, finding hidden gems and exposing them for those who have left the world of hype and are looking for something more real. Rinse and repeat. My teaching is an outlet for myself to put ideas into action, helping others effectively along the way and sharing for those without the same level of experience. I keep falling into other ventures, but am always happiest and most content when returning back to the teaching initiatives." | Python | Chief/Lead Role | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 10 K | ||||||||||||||||
The Comprehensive Programming in R Course | How to design and develop efficient general-purpose R applications for diverse tasks and domains. | 4.3 | 246 | 3147 | Created by Geoffrey Hubona, Ph.D. | Aug-20 | English | $11.99 | 24h 59m total length | https://www.udemy.com/course/the-comprehensive-programming-in-r-course/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | The Comprehensive Programming in R Course is actually a combination of two R programming courses that together comprise a gentle, yet thorough introduction to the practice of general-purpose application development in the R environment. The original first course (Sections 1-8) consists of approximately 12 hours of video content and provides extensive example-based instruction on details for programming R data structures. The original second course (Sections 9-14), an additional 12 hours of video content, provides a comprehensive overview on the most important conceptual topics for writing efficient programs to execute in the unique R environment. Participants in this comprehensive course may already be skilled programmers (in other languages) or they may be complete novices to R programming or to programming in general, but their common objective is to write R applications for diverse domains and purposes. No statistical knowledge is necessary. These two courses, combined into one course here on Udemy, together comprise a thorough introduction to using the R environment and language for general-purpose application development. The Comprehensive Programming in R Course (Sections 1-8) presents an detailed, in-depth overview of the R programming environment and of the nature and programming implications of basic R objects in the form of vectors, matrices, dataframes and lists. The Comprehensive Programming in R Course (Sections 9-14) then applies this understanding of these basic R object structures to instruct with respect to programming the structures; performing mathematical modeling and simulations; the specifics of object-oriented programming in R; input and output; string manipulation; and performance enhancement for computation speed and to optimize computer memory resources. | https://www.udemy.com/course/the-comprehensive-programming-in-r-course/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Artificial Intelligence (AI) in Software Testing | The Future of Automated Testing with Machine Learning - Implementing Artificial Intelligence (AI) in Test Automation | 3.4 | 246 | 862 | Created by Sujal Patel | Jan-19 | English | $9.99 | 1h 31m total length | https://www.udemy.com/course/artificial-intelligence-ai-in-software-testing/ | Sujal Patel | Passionate Test Automation Expert, Consultant and Trainer | 4.1 | 1562 | 26140 | **************************THIS COURSE IS RECENTLY UPDATED with quick course summary contents in the form of pdf files (In Section 11: Summary) to host Lunch and Learns sessions for your friends or coworkers**************************************** The reason behind is, I have received lot of good feedback about this course from different group of peoples. They are really excited to know about how Artificial Intelligence can help in Software Testing. They want to teach their friends or coworkers the importance of Artificial Intelligence in Software Testing. They requested that I can come up with 30-40 min quick presentation from my detail course so they can host lunch and learn session for their friends or coworkers. I liked their idea and that’s why I have created quick pdf document called: Learn the Basic Fundamentals of AI in Software Testing in less than 30 minutes. **************************THIS COURSE IS RECENTLY UPDATED with season 2 course contents. In this season 2, I have added ONE NEW LECTURE CALLED: AI Test Automation Demo using Testim in “Innovative AI Test Automation Tools for the Future” section of the course********************************* HIGHLIGHTS: The NEW LECTURE shows how to create AI Test Automation project for Web Application using TESTIM tool. If your company's application is web application then you can create automation script using TESTIM which uses AI Machine Learning technique. In that video, you will see how you can create automation scripts. You will also see the difference between Coded UI/Selenium scripts and AI scripts. You will be amazed to see that how AI automation scripts PASSED the test execution even if you change the web element all attributes value. Please periodically check out this course since I am also planning to add new topics (Smart API Test Generator - which uses Artificial Intelligence to convert your Web UI tests into Automated API Tests) including replacing some static slides to animated slides. *********************************************************************************************************************** Artificial Intelligence (AI) in Software Testing course is the first ever course on UDemy which talks about future of Automated Testing with AI Machine Learning. I have decided to release this course into two seasons. it requires students to understand basic fundamental of Artificial Intelligence (AI) and the need for AI in Software Testing on first season before we jump into next season where we can deep dive into AI test automation and discussed some innovative tools that we can use for implementing AI in test automation. This course is designed for both testers and developers. Tester who want to develop their testing skills in the test automation with Artificial Intelligence (AI) and Developer who want to execute their unit test in automated way using Artificial Intelligence (AI). This course will teach you how AI-assisted test automation can transform the UI. This course will also teach you Artificial Intelligence (AI) and it's relationship with Machine Learning, Deep Learning and Data Science. After you have completed this course you should be able to build test automation projects for your company's applications using Artificial Intelligence (AI). This course should also help you for your AI test automation job interview. | https://www.udemy.com/course/artificial-intelligence-ai-in-software-testing/#instructor-1 | Sujal is a Software Quality Professional and Test Automation expert with almost 20 years of combined experience in all software engineering life cycle phases of software development, software testing as well as product deployment. He is highly trained individual with a proven track record of delivery, strong strategic vision and demonstrated ability to inspire/mentor/manage QA teams and its activities in the geographically dispersed companies with his outstanding leadership ability and creative problem solving skills. Expertise in test automation suite development using various testing tools like VSTS/MTM/Xamarin, Coded UI, Selenium, Appium, DeviceAnywhere, Protractor, Ranorex, QTP(UFT) and RFT in waterfall and agile development processes. Extensive experience doing performance testing and load testing of web API/web based application using various testing tools like VSTS, HP Load Runner, JMeter/Blazemeter, Fiddler, SoapUI and Postman. In depth knowledge of manual and automated testing of mobile application including mobile sites, native apps and hybrid apps. Sujal currently resides in Chicago USA. In his free time he likes to play cricket and to spend time with family. The road trip is his biggest adventure of the life and he has already covered most of the US states. | Artificial Intelligence | Consultant | >=3 | Below 1K | Below 1K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Artificial Neural Network and Machine Learning using MATLAB | Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts | 4.4 | 245 | 867 | Created by Nastaran Reza Nazar Zadeh | Jul-22 | English | $9.99 | 4h 19m total length | https://www.udemy.com/course/artificial-neural-network-and-machine-learning-using-matlab/ | Nastaran Reza Nazar Zadeh | MS Computer Engineer | 4.4 | 245 | 867 | This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up. In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered. MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN. At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization. | https://www.udemy.com/course/artificial-neural-network-and-machine-learning-using-matlab/#instructor-1 | I have a Master of Science in computer engineering from Mapua University with over seven years of teaching experience in electronic and computer engineering programs in reputable academic institutions. I am a researcher and adviser in the field of robotic and artificial intelligence; and a resource speaker in international events. Currently, I'm the vice president of robotic in the Mechatronics and Robotics Society of the Philippines and active as the "Editor In Chief" for the 2nd International Conference on Automation, Mechatronics, and Robotics. I am eager to share the experience and competence that I have developed as a Professional Engineer and provide the skills required to build some of these incredible systems. | Machine Learning | Engineer/Developer | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 1 K | |||||||||||||||||
The Complete Python and JavaScript Course: Build Projects | Want to learn ES6 development and TensorFlow stock market prediction modeling? Build your first web app in this course! | 4.6 | 245 | 24957 | Created by Mammoth Interactive, John Bura | Jul-18 | English | $13.99 | 27h 12m total length | https://www.udemy.com/course/the-complete-python-and-javascript-course-build-projects/ | Mammoth Interactive | Top-Rated Instructor, 800,000+ Students | 4.3 | 11870 | 0 | "It's a great course for someone with little experience with programming" - Joshua, Mammoth Interactive student "The instructor has proper knowledge and I am on the way of learning process and enjoying it !!!! I recommend for the absolute beginners." "Best course for JavaScript and Python thank you!" - Chirag P. ------------------------------------------------------------------------------------------------------------ This amazing Mammoth Interactive course will make clear to you many obscure concepts on JavaScript, Python and machine learning! Funded by a #1 Kickstarter Project by Mammoth Interactive Python & JS Masterclass: Build TensorFlow & ES6 Projects is detailed coverage you will not get in other Python courses. Our collaborative instructors will teach you impressive application of machine learning in depth and realistic! Enroll now to learn how to develop in PyCharm Community Edition 2017. Python and JS Masterclass: Build TensorFlow and ES6 Projects 27 hours on-demand video! Learn offline via the Udemy app 8 Articles 5 Supplemental Resources Full lifetime access Learn to Code in JavaScript and Python! In Python & JS Masterclass: Build TensorFlow & ES6 Projects, you will learn the fundamentals of coding in JavaScript, including ES6. You will learn how to change what is displayed on a webpage using JavaScript. No prior experience in JavaScript is required. We will explore ES6 in depth and cover many of its new features. You will learn the newest possibilities and fundamental building blocks of JavaScript. Get started with JavaScript basics Learn about ES6 and its new features Apply ES6 concepts in your projects Use build tools like Gulp and Webpack Compile ES6 into ES5 using Babel Predict the Stock Market with Automated Tasks You will learn how to code in Python 3, calculate linear regression with TensorFlow, and make a stock market prediction app. We interweave theory with practical examples so that you learn by doing. Learn how to code in Python, a popular coding language used for websites like YouTube and Instagram. Learn TensorFlow and how to build models of linear regression. Build stock market prediction models for integration into apps! Blend Theory with Hands On Coding Projects! You will learn how to build an impressive model with a single variable. We don't go into daily stock market prediction. Build Powerful Web Apps with ES6 With ES6 (ECMAScript 6th Edition), you can code for the web. ECMAScript is another name for JavaScript. ES6 has standardized features that JavaScript engines implement. ES6 is well-supported across different web browsers. Become a Data Scientist Today You too can become a web developer by learning the popular programming language JavaScript. You'll also learn hands-on Python coding, TensorFlow logistic regression, regression analysis, machine learning, and data science! "This course is definitely helps me on clearing my fundamentals on javascript. It is a good course for beginners but not for the advanced." - Kishan C. Enroll now while on sale! | https://www.udemy.com/course/the-complete-python-and-javascript-course-build-projects/#instructor-1 | Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more. Over 11 years, Mammoth Interactive has built a global student community with 1.1 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content. Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more. John also runs SaaS company Devonian Apps, building efficiency-minded software for technology workers like you. "I absolutely love this course. This is such a comprehensive course that was well worth the money I spent and a lot more. Will definitely be looking at more Mammoth Interactive courses when I finish this." – Student Matt W. "Very good at explaining the basics then building to more complex features." – Student Kevin L. Try a course today. | Python | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=20K | >=4 | Below 1 Lakh | >=10 Lakh | |||||||||||||||||
Tensorflow Deep Learning - Data Science in Python | Tensorflow Deep Learning Python : Tensorflow Neural Network Training : Tensorflow Models - Android Java : Tensorflow C# | 4.7 | 244 | 2867 | Created by Minerva Singh | Nov-22 | English | $11.99 | 7h 20m total length | https://www.udemy.com/course/tensorflow-bootcamp-for-data-science-in-python/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | Complete Tensorflow Mastery For Machine Learning & Deep Learning in Python THIS IS A COMPLETE DATA SCIENCE TRAINING WITH TENSORFLOW IN PYTHON! It is a full 7-Hour Python Tensorflow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learning using the Tensorflow framework in Python.. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE: This course is your complete guide to practical data science using the Tensorflow framework in Python.. This means, this course covers all the aspects of practical data science with Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow is revolutionizing Deep Learning... By storing, filtering, managing, and manipulating data in Python and Tensorflow, you can give your company a competitive edge and boost your career to the next level. THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON TENSORFLOW BASED DATA SCIENCE! But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.. This gives students an incomplete knowledge of the subject. My course, on the other hand, will give you a robust grounding in all aspects of data science within the Tensorflow framework. Unlike other Python courses, we dig deep into the statistical modeling features of Tensorflow and give you a one-of-a-kind grounding in Python based Tensorflow Data Science! DISCOVER 8 COMPLETE SECTIONS ADDRESSING EVERY ASPECT OF PYTHON BASED TENSORFLOW DATA SCIENCE: • A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python • A comprehensive presentation about Tensorflow installation and a brief introduction to the other Python data science packages • Brief introduction to the working of Pandas and Numpy • The basics of the Tensorflow syntax and graphing environment • Statistical modelling with Tensorflow • Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework • You’ll even discover how to create artificial neural networks and deep learning structures with Tensorflow BUT, WAIT! THIS ISN'T JUST ANY OTHER DATA SCIENCE COURSE: You’ll start by absorbing the most valuable Python Tensorflow Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python based data science in real -life. After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python along with gaining fluency in Tensorflow. I will even introduce you to deep learning models such as Convolution Neural network (CNN) !! The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today, start analyzing data for your own projects whatever your skill level, and impress your potential employers with actual examples of your data science abilities. This course will take students without a prior Python and/or statistics background background from a basic level to performing some of the most common advanced data science techniques using the powerful Python based Jupyter notebooks It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.. After each video you will learn a new concept or technique which you may apply to your own projects! JOIN THE COURSE NOW! #tensorflow #python #deeplearning #android #java #neuralnetwork #models | https://www.udemy.com/course/tensorflow-bootcamp-for-data-science-in-python/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Deep Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn | With Exercises : Learn to use NumPy, Pandas, Seaborn , Matplotlib for Data Manipulation and Exploration with Python | 4.7 | 244 | 39369 | Created by Taher Assaf | Oct-21 | English | $9.99 | 6h 42m total length | https://www.udemy.com/course/python-bootcamp-for-data-science-2021-numpy-pandas-seaborn/ | Taher Assaf | Instructer | 4.7 | 675 | 58246 | This course is ideal for you, if you wish is to start your path to becoming a Data Scientist! Data Scientist is one of the hottest jobs recently the United States and in Europe and it is a rewarding career with a high average salary. The massive amount of data has revolutionized companies and those who have used these big data has an edge in competition. These companies need data scientist who are proficient at handling, managing, analyzing, and understanding trends in data. This course is designed for both beginners with some programming experience or experienced developers looking to extend their knowledge in Data Science! I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single skill in data manipulation using Python libraries for data science. In this comprehensive course, I will guide you to learn how to use the power of Python to manipulate, explore, and analyze data, and to create beautiful visualizations. My course is equivalent to Data Science bootcamps that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With over 90 HD video lectures, including all examples presented in this course which are provided in detailed code notebooks for every lecture. This course is one of the most comprehensive course for using Python for data science on Udemy! I will teach you how to use Python to manipulate and to explore raw datasets, how to use python libraries for data science such as Pandas, NumPy, Matplotlib, and Seaborn, how to use the most common data structures for data science in python, how to create amazing data visualizations, and most importantly how to prepare your datasets for advanced data analysis and machine learning models. Here a few of the topics that you will be learning in this comprehensive course: How to Set Your Python Environment How to Work with Jupyter Notebooks Learning Data Structures and Sequences for Data Science In Python How to Create Functions in Python Mastering NumPy Arrays Mastering Pandas Dataframe and Series Learning Data Cleaning and Preprocessing Mastering Data Wrangling Learning Hierarchical Indexing Learning Combining and Merging Datasets Learning Reshaping and Pivoting DataFrames Mastering Data Visualizations with Matplotlib, Pandas and Seaborn Manipulating Time Series Practicing with Real World Data Analysis Example Enroll in the course and start your path to becoming a data scientist today! | https://www.udemy.com/course/python-bootcamp-for-data-science-2021-numpy-pandas-seaborn/#instructor-1 | Having background in science and data modeling, I was interested early during my career with trading forex and trading stock markets. 15 years ago I have started my journey as a private retail trader and I have gone my way through this bumpy road of self-learning and self-development. It did not take me much time to master to some degree many aspects of forex trading that could take years for other beginners to grasp. I was lucky to meet and to have a friendship with many experienced and guru traders, who have shaped in many ways how I do trade forex now. They have opened my eyes and my mind to techniques that are both related to price action analysis and psychological aspects. And this unique knowledge from those incredible insiders not only gave me the turn out that I needed to be successful, but also made me willing to help other traders to bypass the difficult beginning of trading and to help them to shorten their learning curve which is very time- and money consuming for them. I have opened my local trading school around ten years ago with the sole objective was to help traders at their beginning career similar to the help that I have received when I needed it most. | Python | >=4 | Below 1K | >=35K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
The Comprehensive Statistics and Data Science with R Course | Learn how to use R for data science tasks, all about R data structures, functions and visualizations, and statistics. | 4.2 | 242 | 2791 | Created by Geoffrey Hubona, Ph.D. | Oct-19 | English | $9.99 | 19h 40m total length | https://www.udemy.com/course/comprcourse/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | This course, The Comprehensive Statistics and Data Science with R Course, is mostly based on the authoritative documentation in the online "An Introduction to R" manual produced with each new R release by the Comprehensive R Archive Network (CRAN) development core team. These are the people who actually write, test, produce and release the R code to the general public by way of the CRAN mirrors. It is a rich and detailed 10-session course which covers much of the content in the contemporary 105-page CRAN manual. The ten sessions follow the outline in the An Introduction to R online manual and specifically instruct with respect to the following user topics: 1. Introduction to R; Inputting data into R 2. Simple manipulation of numbers and vectors 3. Objects, their modes and attributes 4. Arrays and matrices 5. Lists and data frames 6. Writing user-defined functions 7. Working with R as a statistical environment 8. Statistical models and formulae; ANOVA and regression 9. GLMs and GAMs 10. Creating statistical and other visualizations with R It is a comprehensive and decidedly "hands-on" course. You are taught how to actually use R and R script to create everything that you see on-screen in the course videos. Everything is included with the course materials: all software; slides; R scripts; data sets; exercises and solutions; in fact, everything that you see utilized in any of the 200+ course videos are included with the downloadable course materials. The course is structured for both the novice R user, as well as for the more experienced R user who seeks a refresher course in the benefits, tools and capabilities that exist in R as a software suite appropriate for statistical analysis and manipulation. The first half of the course is suited for novice R users and guides one through "hands-on" practice to master the input and output of data, as well as all of the major and important objects and data structures that are used within the R environment. The second half of the course is a detailed "hands-on" transcript for using R for statistical analysis including detailed data-driven examples of ANOVA, regression, and generalized linear and additive models. Finally, the course concludes with a multitude of "hands-on" instructional videos on how to create elegant and elaborate statistical (and other) graphics visualizations using both the base and gglot visualization packages in R. The course is very useful for any quantitative analysis professional who wishes to "come up to speed" on the use of R quickly. It would also be useful for any graduate student or college or university faculty member who also seeks to master these data analysis skills using the popular R package. | https://www.udemy.com/course/comprcourse/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Statistics | Teacher/Trainer/Professor/Instructor | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Object Detection Web App with TensorFlow, OpenCV and Flask | Build an Object Detection Model from Scratch using Deep Learning and Transfer Learning | 3.2 | 242 | 38064 | Created by Yaswanth Sai Palaghat | Jan-21 | English | $9.99 | 1h 1m total length | https://www.udemy.com/course/object-detection-web-app-with-tensorflow-opencv-and-flask/ | Yaswanth Sai Palaghat | Founder of Techie Empire | 3.8 | 3546 | 159956 | Detecting Objects and finding out their names from images is a very challenging and interesting field of Computer Vision. The core science behind Self Driving Cars, Image Captioning and Robotics lies in Object Detection. In this course, you are going to build a Object Detection Model from Scratch using Python's OpenCV library using Pre-Trained Coco Dataset. The model will be deployed as an Web App using Flask Framework of Python. TECHNOLOGIES & TOOLS USED Python Machine Learning Deep Learning Transfer Learning Tensorflow OpenCV Flask | https://www.udemy.com/course/object-detection-web-app-with-tensorflow-opencv-and-flask/#instructor-1 | Hey techies, I am Yaswanth Sai Palaghat. I am a content creator, Digital Marketer and a software developer from Hyderabad and a freelancer. I have a youtube channel named "YASWANTH SAI PALAGHAT" where I regularly upload videos on tech, career development, personal finance, Interview Preparation, and many more. | Tensor Flow | Founder/Entrepreneur | >=3 | Below 1K | >=35K | >=3 | Below 10 K | >=1.5 Lakh | |||||||||||||||||
Computer Vision: YOLO Custom Object Detection with Colab GPU | YOLO: Pre-Trained Coco Dataset and Custom Trained Coronavirus Object Detection Model with Google Colab GPU Training | 4.7 | 242 | 3673 | Created by Abhilash Nelson | Feb-22 | English | $11.99 | 3h 59m total length | https://www.udemy.com/course/computer-vision-yolo-custom-object-detection-with-colab-gpu/ | Abhilash Nelson | Computer Engineering Master & Senior Programmer at Dubai | 4.2 | 2186 | 41457 | Hi There! welcome to my new course 'YOLO Custom Object Detection Quick Starter with Python'. This is the fourth course from my Computer Vision series. As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image. We will be specifically focusing on (YOLO), You only look once which is an effective real-time object recognition algorithm which is featured in Darknet, an open source neural network framework This course is equally divided into two halves. The first half will deal with object recognition using a predefined dataset called the coco dataset which can classify 80 classes of objects. And the second half we will try to create our own custom dataset and train the YOLO model. We will try to create our own coronavirus detection model. Let's now see the list of interesting topics that are included in this course. At first we will have an introductory theory session about YOLO Object Detection system. After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine. Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures. Then we will install install OpenCV, which is the Open Source Computer Vision library in Python. Then we will have an introduction to Convolutional Neural Networks , its working and the different steps involved. Now we will proceed with the part 1 that involves Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image. Often YOLO gives back more than one successful detection for a single object in an image. This can be fixed using a technique called as NMS or Non Maxima Suppression. We will implement that in our next session. And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file. Then we will proceed with part 2 of the course in which we will attempt to train a darknet YOLO model. A model which can detect coronavirus from an electron microscope image or video output. Before we proceed with the implementation, we will discuss the pros and cons of using a pre-trained dataset model and a custom dataset trained model. Also about the free GPU offered by google colab and its features. In the next session we will start with phase 1 of our custom model in which we will do the preparation steps to implement custom model. We will at first download the darknet source from github and prepare it. We will then download the weight files required for both testing and training. And then we will edit the required configurations files to make it ready for our custom coronavirus detector. In the second phase for our custom model, we will start collecting the required data to train the model. We will collect coronavirus images from the internet as much as we could and organize them into folder. Then we will label or annotate the coronavirus object inside these images using an opensource annotation tool called labelImg. Then we will split the gathered dataset, 80% for training and 20% for testing. And finally will edit the prepare the files with the location of training and testing datasets. Now that we have all our files ready, in our third phase, we will zip and upload them into google drive. After that we will create a google colab notebook and configure the colab runtime to use the fast, powerful, yet free GPU service provided by google. Then we will mount our google drive to our colab runtime and unzip the darknet zip we uploaded. Sometimes files edited in non unix environments may be having problems when compiling the darknet. We have to convert the encoding from dos to unix as our next step. Then we will complile the darknet framework source code and proceed with testing the darknet framework with a sample image in our fourth phase. The free GPU based runtime provided by google colab is volatile. It will get reset every 12 hours. So we need to save our weights periodically during training to our google drive which is a permanent storage. So in our phase five, we will link a backup folder in google drive to the colab runtime. Finally in our phase 6, we are ready to proceed with training our custom coronavirus model. We will keep on monitoring the loss for every iteration or epoch as we call it in nerual network terms. Our model will automatically save the weights every 100th epoch securely to our google drive backup folder. We can see a continues decrease in the loss values as we go through the epoch. And after many number of iterations, our model will come into a convergence or flatline state in which there is no further improvement in loss. at that time we will obtain a final weight Later we will use that weight to do prediction for an image that contains coronavirus in it. We can see that our model clearly detects objects. We will even try this with a video file also. We cannot claim that its a fully fledged flawless production ready coronavirus detection model. There is still room for improvement. But anyway, by building this custom model, we came all the way through the steps and process of making a custom yolo model which will be a great and valuable experience for you. And then later in a quick session, we will also discuss few other case studies in which we can implement a custom trained YOLO model, the changes we may need to make for training those models etc. That's all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked. Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio. So that's all for now, see you soon in the class room. Happy learning and have a great time. | https://www.udemy.com/course/computer-vision-yolo-custom-object-detection-with-colab-gpu/#instructor-1 | I am a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. I am a Post Graduate Masters Degree holder in Computer Science and Engineering. My experience with PHP/Python Programming is an added advantage for server based Android and iOS Client Applications. I am currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design. | Computer Vision | Senior Role | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Big Data Programming Languages & Big Data Vs Data Science | Big Data Programming Languages,Skills to become a Big Data Professional,Differences between Big Data & Data Science | 3.7 | 240 | 28723 | Created by Lalitha Audikesavan | Apr-20 | English | $9.99 | 36m total length | https://www.udemy.com/course/big-data-programming-languages-big-data-vs-data-science/ | Lalitha Audikesavan | Lalitha Audikesavan with 14 years of Database IT experience | 3.8 | 1103 | 62990 | Students will learn the following topics in this course. Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data Big Data Programming Languages Programming Language Concepts Skills to become a Big Data Professional Differences between Big Data & Data Science Similarities Between Big Data & Data Science Challenges of Big Data | https://www.udemy.com/course/big-data-programming-languages-big-data-vs-data-science/#instructor-1 | I am Lalitha Audikesavan having 14 years of experience in Information Technology industry and currently a freelance online trainer teaching many courses on Udemy. I can be connected on linkedin. I have extensive work experience on SQL SERVER Database Administration, SQL SERVER programming, VISUAL BASIC programming and Project Delivery Operations Management. I have knowledge on ITIL, BIG DATA, HADOOP, MongoDB, Python, Java, ASP, Oracle programming and Cloud also. I have completed Certification in ITIL V3 with 100/100 score in ITIL EXIN certification exam. I am also Brainbench certified in Server Administration, SQL ANSI fundamentals and SQL Server Database Administration. Got First class with Distinction grade in B.E ( Bachelor of Engineering ) in Computer Science. | Big Data/Data Engineer | >=3 | Below 1K | >=25K | >=3 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Machine Learning In The Cloud With Azure Machine Learning | Introduction to machine learning in the cloud with Azure Machine Learning. | 4.1 | 240 | 5565 | Created by TetraNoodle Team, Manuj Aggarwal | Feb-19 | English | $9.99 | 3h 0m total length | https://www.udemy.com/course/machine-learning-in-the-cloud-with-azure-machine-learning/ | TetraNoodle Team | REAL KNOWLEDGE. REAL EXPERIENCE. REAL VALUE. | 4.1 | 27897 | 210025 | The history of data science, machine learning, and artificial Intelligence is long, but it’s only recently that technology companies - both start-ups and tech giants across the globe have begun to get excited about it… Why? Because now it works. With the arrival of cloud computing and multi-core machines - we have enough compute capacity at our disposal to churn large volumes of data and dig out the hidden patterns contained in these mountains of data. This technology comes in handy, especially when handling Big Data. Today, companies collect and accumulate data at massive, unmanageable rates for website clicks, credit card transactions, GPS trails, social media interactions, and so on. And it is becoming a challenge to process all the valuable information and use it in a meaningful way. This is where machine learning algorithms come into the picture. These algorithms use all the collected “past” data to learn patterns and predict results or insights that help us make better decisions backed by actual analysis. You may have experienced various examples of Machine Learning in your daily life (in some cases without even realizing it). Take for example Credit scoring, which helps the banks to decide whether to grant the loans to a particular customer or not - based on their credit history, historical loan applications, customers’ data and so onOr the latest technological revolution from right from science fiction movies – the self-driving cars, which use Computer vision, image processing, and machine learning algorithms to learn from actual drivers’ behavior. Or Amazon's recommendation engine which recommends products based on buying patterns of millions of consumers. In all these examples, machine learning is used to build models from historical data, to forecast the future events with an acceptable level of reliability. This concept is known as Predictive analytics. To get more accuracy in the analysis, we can also combine machine learning with other techniques such as data mining or statistical modeling. This progress in the field of machine learning is great news for the tech industry and humanity in general. But the downside is that there aren’t enough data scientists or machine learning engineers who understand these complex topics. Well, what if there was an easy to use a web service in the cloud - which could do most of the heavy lifting for us? What if scaled dynamically based on our data volume and velocity? The answer - is new cloud service from Microsoft called Azure Machine Learning. Azure Machine Learning is a cloud-based data science and machine learning service which is easy to use and is robust and scalable like other Azure cloud services. It provides visual and collaborative tools to create a predictive model which will be ready-to-consume on web services without worrying about the hardware or the VMs which perform the calculations. The advantage of Azure ML is that it provides a UI-based interface and pre-defined algorithms that can be used to create a training model. And it also supports various programming and scripting languages like R and Python. In this course, we will discuss Azure Machine Learning in detail. You will learn what features it provides and how it is used. We will explore how to process some real-world datasets and find some patterns in that dataset. Do you know what it takes to build sophisticated machine learning models in the cloud? How to expose these models in the form of web services? Do you know how you can share your machine learning models with non-technical knowledge workers and hand them the power of data analysis? These are some of the fundamental problems data scientists and engineers struggle with on a daily basis. This course teaches you how to design, deploy, configure and manage your machine learning models with Azure Machine Learning. The course will start with an introduction to the Azure ML toolset and features provided by it and then dive deeper into building some machine learning models based on some real-world problems If you’re serious about building scalable, flexible and powerful machine learning models in the cloud, then this course is for you. These data science skills are in great demand, but there’s no easy way to acquire this knowledge. Rather than rely on hit and trial method, this course will provide you with all the information you need to get started with your machine learning projects. Startups and technology companies pay big bucks for experience and skills in these technologies They demand data science and cloud engineers make sense of their dormant data collected on their servers - and in turn, you can demand top dollar for your abilities. You may be a data science veteran or an enthusiast - if you invest your time and bring an eagerness to learn, we guarantee you real, actionable education at a fraction of the cost you can demand as a data science engineer or a consultant. We are confident your investment will come back to you many-fold in no time. So, if you're ready to make a change and learn how to build some cool machine learning models in the cloud, click the "Add to Cart" button below. Look, if you're serious about becoming an expert data engineer and generating a greater income for you and your family, it’s time to take action. Imagine getting that promotion which you’ve been promised for the last two presidential terms. Imagine getting chased by recruiters looking for skilled and experienced engineers by companies that are desperately seeking help. We call those good problems to have. Imagine getting a massive bump in your income because of your newly-acquired, in-demand skills. That’s what we want for you. If that’s what you want for yourself, click the “Add to Cart” button below and get started today with our “Machine Learning In The Cloud With Azure Machine Learning”. Let’s do this together! | https://www.udemy.com/course/machine-learning-in-the-cloud-with-azure-machine-learning/#instructor-1 | At TetraNoodle Technologies and its education arm - TetraTutorials, we work with several startups and build small to very high scale cloud solutions every day. We know what it takes to do this well. We strive to put all our hands-on experience into these courses. Instead of superficial knowledge - we go into the depth of the topic and give you the exact - step by step blueprint on how to tame these complex topics in easy and digestible bite-sized videos. This real world knowledge enables you to grasp these concepts easily, and you can apply this learning immediately into your projects. TetraNoodle technologies has been in the software business since 2001. We have been part of many prestigious projects and startups. Over the course of these years - we have gained a good insight into what makes for flexible, scalable and robust software solutions. We are passionate about sharing all our collective knowledge with you. As of mid-2017, we have already taught over TWENTY FIVE THOUSAND students and counting. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||||
Linear Algebra and Feature Selection in Python | Acquire the Theoretical and Practical Foundations That Would Allow You to Learn Machine Learning With Understanding | 4.4 | 238 | 1482 | Created by 365 Careers | Mar-22 | English | $9.99 | 2h 55m total length | https://www.udemy.com/course/linear-algebra-and-feature-selection-in-python/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | Do you want to learn linear algebra? You have come to the right place! First and foremost, we want to congratulate you because you have realized the importance of obtaining this skill. Whether you want to pursue a career in data science, machine learning, data analysis, software engineering, or statistics, you will need to know how to apply linear algebra. This course will allow you to become a professional who understands the math on which algorithms are built, rather than someone who applies them blindly without knowing what happens behind the scenes. But let’s answer a pressing question you probably have at this point: “What can I expect from this course and how it will help my professional development?” In brief, we will provide you with the theoretical and practical foundations for two fundamental parts of data science and statistical analysis – linear algebra and dimensionality reduction. Linear algebra is often overlooked in data science courses, despite being of paramount importance. Most instructors tend to focus on the practical application of specific frameworks rather than starting with the fundamentals, which leaves you with knowledge gaps and a lack of full understanding. In this course, we give you an opportunity to build a strong foundation that would allow you to grasp complex ML and AI topics. The course starts by introducing basic algebra notions such as vectors, matrices, identity matrices, the linear span of vectors, and more. We’ll use them to solve practical linear equations, determine linear independence of a random set of vectors, and calculate eigenvectors and eigenvalues, all preparing you for the second part of our learning journey - dimensionality reduction. The concept of dimensionality reduction is crucial in data science, statistical analysis, and machine learning. This isn’t surprising, as the ability to determine the important features in a dataset is essential - especially in today’s data-driven age when one must be able to work with very large datasets. Imagine you have hundreds or even thousands of attributes in your data. Working with such complex information could lead to a variety of problems – slow training time, the possibility of multicollinearity, the curse of dimensionality, or even overfitting the training data. Dimensionality reduction can help you avoid all these issues, by selecting the parts of the data which actually carry important information and disregarding the less impactful ones. In this course, we’ll discuss two staple techniques for dimensionality reduction – Principal Components Analysis (PCA), and Linear Discriminant Analysis (LDA). These methods transform the data you work with and create new features that carry most of the variance related to a given dataset. First, you will learn the theory behind PCA and LDA. Then, going through two complete examples in Python, you will see how data transformation occurs in practice. For this purpose, you will get one step-by-step application of PCA and one of LDA. Finally, we will compare the two algorithms in terms of speed and accuracy. We’ve put a lot of effort to make this course the perfect foundational training for anyone who wants to become a data analyst, data scientist, or machine learning engineer. | https://www.udemy.com/course/linear-algebra-and-feature-selection-in-python/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Python | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||||
Deep Learning with TensorFlow | Channel the power of deep learning with Google's TensorFlow! | 4 | 237 | 2043 | Created by Packt Publishing | Aug-17 | English | $9.99 | 2h 0m total length | https://www.udemy.com/course/deep-learning-with-tensorflow/ | Packt Publishing | Tech Knowledge in Motion | 3.9 | 68744 | 404808 | With deep learning going mainstream for making sense of data, getting accurate results using deep networks is possible. This video is your guide to explore possibilities with deep learning. It will enable you to understand data like never before. With efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights which would change how you look at data. With this video, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This video will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the video course, you will come across topics like logistic regression, convolutional neural networks, training deep networks, and so on. With the help of practical examples, the video will cover advanced multilayer networks, image recognition, and beyond. This course uses TensorFlow 0.8 and Python 3.5, while not the latest version available, it provides relevant and informative content for legacy users of TensorFlow, and Python. About The Author Dan Van Boxel is a Data Scientist and Machine Learning Engineer with over 10 years of experience. He is most well-known for "Dan Does Data," a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals. | https://www.udemy.com/course/deep-learning-with-tensorflow/#instructor-1 | Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development. Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge. We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them. | Deep Learning | >=4 | Below 1K | Below 10K | >=3 | Below 1 Lakh | >=4 Lakh | ||||||||||||||||||
Digishock 2.0: Learn Machine Learning in 2022 (No Coding) | Get ready to learn and master no-code machine learning tools in 2022 | 2.9 | 237 | 45357 | Created by Srinidhi Ranganathan, Vindhya AR | Sep-22 | English | $9.99 | 56m total length | https://www.udemy.com/course/digishock-20-machine-learning-for-beginners-no-coding/ | Srinidhi Ranganathan | Digital Marketing Consultant | 3.9 | 24186 | 907599 | Welcome to experience "Digishock 2.0: Learn Machine Learning in 2022" in a new interactive and engaging way. We all know that Machine learning is the actual application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experience. Let us elaborate on this further. Machine learning is a subset of artificial intelligence (AI) that allows systems to learn and improve on their own without having to be explicitly programmed. Different algorithms (e.g. neural networks) are used in machine learning to address problems. The ever-trending field of machine learning is primarily focused on the development of computer coded programs that can access data and make machines learn themselves to perform mundane tasks autonomously. 'Autonomously' means that the task is not fully controlled by humans and machines play a bigger role in managing or scheduling them. Why learn "Digishock 2.0: The Ultimate Machine Learning Course of all time"? This mind-blowing 2022 course - the second course in the series of MBA in Programming and Trending Technologies titled "Digishock 2.0: Learn Machine Learning in 2022 (No Coding)" taught by Digital Marketing Legend "Srinidhi Ranganathan" and "Vindhya" takes the huge leap from Digishock 1.0 and is for anyone who wants to get introduced to Machine Learning and Deep Learning without learning code, whatsoever. "Digishock 2.0" involves hands-on exercises with numerous tricks and techniques of analytics, advanced predictive concepts to work on to ensure that all are familiarised with the discipline of machine-learning, deep-learning, big data, analytics, etc. The USP of the course is that there is no kind of technical knowledge required whatsoever for students who will participate in this course. What's more? We also will cover the topics of how you can create an app in minutes based on any software or application and start the conversion process. You can explore the techniques to create a voice assistant in minutes like Amazon's Alexa and deploy the same for Raspberry Pi. These inspiring and fast-track methods or techniques would help you gain new knowledge of this vast subject. Nevertheless, some of these product outputs you will receive from the tech tools taught here in the course can also be used for machine learning projects you would be submitting for your college university. Enroll now in this mind-blowing machine learning course and be amazed by the technology and techniques taught. Let's start booming. Good luck with learning new things. | https://www.udemy.com/course/digishock-20-machine-learning-for-beginners-no-coding/#instructor-1 | Important Note: Feel free to connect with Srinidhi on LinkedIn anytime. Catch some secretive educational videos created by Srinidhi on YouTube to further help in your learning by clicking the button on the right. About Srinidhi Ranganathan: Digital Marketing Consultant and Marketing Legend "Srinidhi Ranganathan" is the Chief Executive Officer (CEO) and Managing Director of First Look Digital Marketing Solutions (India's First Artificial Intelligence Powered Digital Marketing company) located in Bangalore and is one of the top instructors in India who is teaching highly futuristic digital marketing-related courses on Udemy. He is a Technologist, Digital Marketing Coach, Author, and Video Creation Specialist with over 10+ years of AIDM experience and has worked at top companies in India. Using his innovative marketing expertise, Srinidhi provides consulting services to startups and established brands utilising strategic planning and an extensive marketing audit powered by AI. He deploys the most comprehensive digital marketing strategy to clients worldwide that takes into account KPIs, methodology, and research statistics (utilising competitive intelligence software). Creating a growth hacking plan, content strategy, marketing mix, target segmentation analysis, competitor case-studies, brand strategy, local and global market research are also part of the consulting process to speed up a typical company's growth. Digital Marketing Legend "Srinidhi Ranganathan" has also helped startups and companies to leverage the best digital marketing strategies powered by automation to multi-fold their revenues. Having over 900,000+ students on Udemy - he has facilitated digital marketing analysis and provided state-of-the-art marketing strategy ideas and tactical execution plans for top marketing companies in India including startups, SMB's and MNC's. This includes strategic brainstorming sessions, Artificial Intelligence-powered market analysis, market research related to digital performance, support of various AIDM marketing initiatives for new product and consumer promotional launches, etc. He uses real-time forecasting, predictive modelling, machine learning, advanced machine learning-based optimisation techniques for business, marketing, Artificial Intelligence (AI) driven customer engagement strategies, competition monitoring software and other world-class tools. Srinidhi gained popularity through the unique, practical yet engaging training methodologies he utilises to teach during the training sessions. Some of his training methods include gamified learning experiences conducted by virtual writing and teaching robots like "Aera 2.0" that prompt behavioural changes in students and bring forth a new kind of fascination among the crowd. These robots are virtual humans having super-intelligence capabilities. They can autonomously train anyone on topics ranging from ABC to Rocket Science, without human intervention. Srinidhi's passionate fans call him a "Digital Marketing Legend" and he's busy working on creating new virtual and humanoid robots to revolutionise education in India and the world in 2022. He is deemed to be an innovator in the field of Artificial Intelligence (AI) based Digital Marketing and is someone who has embraced many ideas and has created various environments in which team members are taught the required AI automation tools and resources to challenge the status quo, push boundaries and achieve super-extensive growth. His courses are a testament to where the future is actually heading. "My goal has always been to give my students the AI tools to be able to leverage their digital marketing experiences, tools that allow them to build marketing success, from a whole new innovative perspective." — Srinidhi Ranganathan Srinidhi is currently working on these ultra-futuristic advancements in 2022 and creating research papers that contain information that delves 100-300 years into the future: 1) Autonomous Self-Thinking Scientific Computers 2) Personal Teleportation through Photons 3) Rise of Thought-to-Text and Dream Recognition Machines 4) Memory Regeneration with Nanobots 5) Birth of Hyper-Reality 6) Virtual Robo-Babies 7) Space-Tourism for the Wealthy 8) Mini-Flying Cities, Creation and Expansion using Maglev Technology 9) Self-Driving Flying Cars 10) Holographic Pets 11) Space-Probes (Stellar Light-Travelling Machines) 12) Virtual Digital Humans (Mind-Clones) 13) Computer-Like Lifeforms 14) Decentralised Artificial Intelligence (AI) Technologies 15) Mind-Revealing Technology 16) Cyborgs at the Workplace 17) Invisible Food 18) Temperature-Adaptable Smart Wearable Clothing 19) Holodeck-style Underwater Starship Homes 20) Yottabyte Storage Tech for Unimaginable Cloud Data Storage 21) Atmospheric Water Generation Secrets (2022-2050) | Machine Learning | Consultant | >=2 | Below 1K | >=45K | >=3 | Below 1 Lakh | >=5 Lakh | |||||||||||||||||
Data Science:Hands-on Covid19 Face Mask Detection-CNN&OpenCV | A Practical Hands-on Data Science Guided Project on Covid-19 Face Mask Detection using Deep Learning & OpenCV | 4 | 236 | 10695 | Created by School of Disruptive Innovation | Nov-20 | English | $9.99 | 1h 45m total length | https://www.udemy.com/course/data-science-hands-on-covid19-face-mask-detection-cnn-opencv/ | School of Disruptive Innovation | Creative Learning Solutions for the Digital Age | 4.1 | 1049 | 42339 | Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places? Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask? If the answer to any of the above questions is "YES", then this course is for you. Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using Tensorflow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely. To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places. This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say : "Short, sweet, to the point course" The same techniques can be used in : Skin cancer detection Normal pneumonia detection Brain defect analysis Retinal Image Analysis Enroll now and You will receive a CERTIFICATE OF COMPLETION and we encourage you to add this project to your resume. At a time when the entire world is troubled by Coronavirus, this project can catapult your career to another level. So bring your laptop and start building, training and testing the Data Science Covid 19 Convolutional Neural Network model right now. You will learn: How to detect Face masks on the static images as well as in the video streams. Classify people who are wearing masks or not using deep learning Learn to Build and train a Convolutional neural network Make a prediction on new data using the trained CNN Model We will be completing the following tasks: Task 1: Getting Introduced to Google Colab Environment & importing necessary libraries Task 2: Downloading the dataset directly from the Kagge to the Colab environment. Task :3 Data visualization (Image Visualization) Task 4: Data augmentation & Normalization Task 5: Building Convolutional neural network model Task 6: Compiling & Training CNN Model Task 7: Performance evaluation & Testing the model & saving the model for future use Task 8: Make use of the trained model to detect face masks on the static image uploaded from the local system Task 9: Make use of the trained model to detect face masks on the video streams So, grab a coffee, turn on your laptop, click on the ENROLL NOW button, and start learning right now. | https://www.udemy.com/course/data-science-hands-on-covid19-face-mask-detection-cnn-opencv/#instructor-1 | Welcome to the School of the Disruptive Innovation. We are here to teach you what they don't teach you in school. We are unconventional in our ways but we promise and we over-deliver. We have a community of over 40,000+ students and 60,000+ enrollments across 166 countries. We offer courses on Data Science (Classical machine Learning, Deep learning, BigData, Data Visualization & Analysis), Android Development, Web Development, and Graphics Design. Every course is created and delivered by professionals in the field such as Technology related courses by software engineers and business related courses are created by business experts. | Misc | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
How to Become A Data Scientist Using Azure Machine Learning | A Practical Introduction To Microsoft's Azure Machine Learning Tools | 3.9 | 231 | 1188 | Created by Mike West | Dec-15 | English | $9.99 | 1h 11m total length | https://www.udemy.com/course/azure-machine-learning-introduction/ | Mike West | Creator of LogikBot | 4.3 | 19947 | 236930 | There can be little doubt that the single hottest career in the data field is the data scientist or BI developer skilled in predictive analytics. Yes, Big Data is on everyone’s lips but what happens after that big data is ingested into a data lake? The answer is predictive analytics. Because we live in the big data era, machine learning has become much more popular in the last few years. Having lots of data to work with in many different areas lets the techniques of machine learning be applied to a broader set of problems. Data can hold secrets, especially if you have lots of it. With lots of data about something, you can examine that data in intelligent ways to find patterns. This is exactly what machine learning does: It examines large amounts of data looking for patterns, then generates code that lets you recognize those patterns in new data. Your applications can use this generated code to make better predictions. In other words, machine learning can help you create smarter applications. Azure Machine Learning (Azure ML) is a cloud service that helps people execute the machine learning process. As its name suggests, it runs on Microsoft Azure, a public cloud platform. Because of this, Azure ML can work with very large amounts of data and be accessed from anywhere in the world. Using it requires just a web browser and an internet connection. In this course you will be learning and building predictive algorithms using Azure Machine Learning Studio. At the end of this course you’ll be able to build and evaluate a binary classification predictive model without authoring a single line of code You’ll build an Experiment for a targeted email campaigned and be able to tell what customers should receive flyers and those that shouldn’t. Thanks for reading about Azure Machine Learning Studio and I’ll see you in the course. | https://www.udemy.com/course/azure-machine-learning-introduction/#instructor-1 | I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help. I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space. Experience, education and passion I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car. Certifications Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT). Personal Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children. | Machine Learning | >=3 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||||
Python Interactive Dashboards with Plotly Dash (new version) | Learn to create interactive Python dashboards (data visualizations) using plotly Dash, with real-world example datasets | 4.7 | 231 | 1077 | Created by Lianne and Justin (Just into Data) | Oct-22 | English | $9.99 | 5h 28m total length | https://www.udemy.com/course/python-interactive-dashboards-with-plotly-dash/ | Lianne and Justin (Just into Data) | Data Scientists | 4.6 | 390 | 1854 | Welcome to your plotly Dash course! So you've got some hard analysis done, how can you nicely present them too? Dash and Plotly in Python can help! They empower you to visualize your critical insights and KPIs in web apps that are easily sharable. Following this course, you'll learn to build dashboards from scratch, by customizing their look and adding interactive features, with all free Python libraries. Throughout the course, you'll be using three real-world datasets to create dashboards. They will make your learning experience more practical. So that you can easily apply the skills to build your own dashboards after taking this course. Data visualization is critical for data science. Don't miss the opportunity to learn this new skill of creating dashboards with Python! This course includes instructional videos that walk you through the process step-by-step. You can learn at your own pace, and download the Python scripts to use for your own projects. Besides Dash, you'll also get a chance to use other key data science libraries, including pandas and plotly. It's ok if you haven't used them before. We'll break down and explain the process within the course. So that you can still follow along! Cheers, Lianne and Justin Preview image designed by freepik | https://www.udemy.com/course/python-interactive-dashboards-with-plotly-dash/#instructor-1 | Justin: an experienced data scientist in many different fields, such as marketing, anti-money laundering, and big data technologies. He also has a bachelor’s degree in computer engineering and a master’s degree in statistics. Lianne: an experienced statistician who has worked in the central bank as well as commercial banks, where she monitored major financial institutions and conducted fraud analysis. She has both a bachelor’s and a master’s degree in statistics. | Python | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Dell Boomi AtomSphere - IPaaS Beginner Training | Master the fundamentals of Dell Boomi AtomSphere | Bestseller | 4.6 | 230 | 1180 | Created by Saad Qureshi | Mar-21 | English | $13.99 | 5h 19m total length | https://www.udemy.com/course/dellboomi/ | Saad Qureshi | Computer Scientist || Freelancer || Programmer | 4.4 | 1757 | 11892 | What is Dell Boomi AtomSphere? Dell Boomi is a cloud integration platform which is used to integrate different applications. What are we learning the in the course? Disk connector Database connector HTTP connector Webservice SOAP client connector Web service Server Connector Set properties and much more After this Course: Once your are done with the course, you will have maximum knowledge of Dell Boomi Cloud Integration platform and can easily apply concepts to create multiple different integrations. Career Perspective: If you want to pursue a career in the field of Data Integration Engineer, Please enroll in course. In this course, we will make your foundation by starting from very basic and then later in the course, we'll cover some advance topics , so, in-case you are very new or have no knowledge of Cloud integration platforms ,by the end this course, you will attain maximum knowledge of this tool. Another aspect of this course is that not only I am going through all the concepts but also give the practical demonstration. The pace of this course is very slow, means, I will emphasize ample time on the subject and will try to cover everything that I can. Boomi was founded in 2000, beginning with "configuration-based" integration. Its technology allows users to build and deploy integration processes using a visual interface and a drag and drop technique. Have a Great Learning..!!! | https://www.udemy.com/course/dellboomi/#instructor-1 | Background: Computer Scientist with several years of industry experience. Other than this,I am passionate about teaching and guiding students learning programming languages. Life Philosophy 1: He who is not courageous enough to take risks will accomplish nothing in life. 2: Do not dwell in the past,do not dream of the future,concentrate the mind on the present moment. 3: Service to others is the rent you pay for your room here on earth.. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Statistics Masterclass for Data Science and Data Analytics | Build a Solid Foundation of Statistics for Data Science, Learn Probability, Distributions, Hypothesis Testing, and More! | 4.4 | 229 | 1153 | Created by Vijay Gadhave | Nov-22 | English | $9.99 | 5h 9m total length | https://www.udemy.com/course/statistics-for-data-science-and-analytics-masterclass/ | Vijay Gadhave | Data Scientist and Software Developer | 4.4 | 1848 | 59213 | Starting a career in Data Science or Business Analysis ? then this course will help you to Built a Strong Foundation of statistics for Data Science and Business Analytics This course is Very Practical, Easy to Understand and Every Concept is Explained with an Example ! I have added real life examples to understand the applications of statistics in the field of Data Science... We'll cover everything that you need to know about statistics and probability for Data Science and Business Analytics ! Including: 1) Levels of Measurement 2) Measures of Central Tendency 3) Population and Sample 4) Population Standard Variance 5) Quartiles and IQR 6) Permutations,Combinations 7) Intersection, Union and Complement 8) Independent and Dependent Events 9) Conditional Probability 10) Bayes’ Theorem 11) Uniform Distribution, Binomial Distribution 12) Poisson Distribution, Normal Distribution, Skewness 13) Standardization and Z Score 14) Central Limit Theorem 15) Hypothesis Testing, Type I and Type II Error 16) Students T-Distribution 17) ANOVA - Analysis of Variance 18) F Distribution 19) Linear Regression and much more... So What Are You Waiting For ? Enroll Now and Empower Your Career ! | https://www.udemy.com/course/statistics-for-data-science-and-analytics-masterclass/#instructor-1 | Hello Data Lover, I am glad that you are reading this! I am Vijay Gadhave and I have 10+ years of experience in the IT Industry. I am passionate about Cloud Computing and Machine Learning. I teach in areas of Cloud Computing, Machine Learning, Python, Data Science, and Data analysis. I hope you will enjoy my course and it will help you to grow in your career. | Data Analyst | Data Scientist | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Deploy Machine Learning Models on GCP + AWS Lambda (Docker) | How to Serialize - Deserialize model with scikit-learn & Deployment on Heroku, AWS Lambda, ECS, Docker and Google Cloud | 4.8 | 230 | 2737 | Created by Ankit Mistry, Data Science & Machine Learning Academy | Oct-21 | English | $9.99 | 4h 17m total length | https://www.udemy.com/course/deploy-machine-learning-model/ | Ankit Mistry | Software Developer | I want to Improve your life & Income. | 4.4 | 6134 | 82664 | Hello everyone, welcome to one of the most practical course on Machine learning and Deep learning model deployment production level. What is model deployment : Let's say you have a model after doing some rigorous training on your data set. But now what to do with this model. You have tested your model with testing data set that's fine. You got very good accuracy also with this model. But real test will come when live data will hit your model. So This course is about How to serialize your model and deployed on server. After attending this course : you will be able to deploy a model on a cloud server. You will be ahead one step in a machine learning journey. You will be able to add one more machine learning skill in your resume. What is going to cover in this course? 1. Course Introduction In this section I will teach you about what is model deployment basic idea about machine learning system design workflow and different deployment options are available at a cloud level. 2. Flask Crash course In this section you will learn about crash course on flask for those of you who is not familiar with flask framework as we are going to deploy model with the help of this flask web development framework available in Python. 3. Model Deployment with Flask In this section you will learn how to Serialize and Deserialize scikit-learn model and will deploy owner flask based Web services. For testing Web API we will use Postman API testing tool and Python requests module. 4. Serialize Deep Learning Tensorflow Model In this section you will learn how to serialize and deserialize keras model on Fashion MNIST Dataset. 5. Deploy on Heroku cloud In this section you will learn how to deploy already serialized flower classification data set model which we have created in a last section will deploy on Heroku cloud - Pass solution. 6. Deploy on Google cloud In this section you will learn how to deploy model on different Google cloud services like Google Cloud function, Google app engine and Google managed AI cloud. 7. Deploy on Amazon AWS Lambda In this section, you will learn how to deploy flower classification model on AWS lambda function. 8. Deploy on Amazon AWS ECS with Docker Container In This section, we will see how to put application inside docker container and deploy it inside Amazon ECS (Elastic Container Services) This course comes with 30 days money back guarantee. No question ask. So what are you waiting for just enroll it today. I will see you inside class. Happy learning Ankit Mistry | https://www.udemy.com/course/deploy-machine-learning-model/#instructor-1 | I am Ankit Mistry, completed my master from IIT Kharagpur in area of machine learning, Artificial intelligence. Now working as Software Developer, Big Data Engineer in one of leading private investment bank with 8+ years of experience in software industry. Over the time I developed interest related to data discipline and learned about data analysis, machine learning model development, Cloud Computing. Created course in area of Cloud Computing, Google Cloud, Python, Data Science, Data analysis, Machine Learning. I am so excited to be on Udemy online learning platform and want to make big impact on your software career. I hope you will like my course offering. | Machine Learning | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Automated Multiple Face Recognition AI Using Python | Learn about OpenCv Basics, Face Recognition in an image, Automation of Face Recognition System using User Inputs | 2.5 | 227 | 19678 | Created by Nishit Maru, Three Millennials | Nov-19 | English | $9.99 | 1h 54m total length | https://www.udemy.com/course/automated-multiple-face-recognition-ai-using-python/ | Nishit Maru | Engineer, Programmer, Developer and Teacher | 3.2 | 245 | 85930 | Hello, welcome to the Amazing world of Computer Vision. Computer Vision is an AI based, that is, Artificial Intelligence based technology that allows computers to understand and label images. Its now used in Convenience stores, Driver-less Car Testing, Security Access Mechanisms, Policing and Investigations Surveillance, Daily Medical Diagnosis monitoring health of crops and live stock and so on and so forth..Even to analyze data coming from outer space stars, planets etc also we use Computer Vision. A common example will be face detection and recognition and unlocking mechanism that you use in your mobile phone. We use that daily. That is also a big application of Computer Vision. And today, top technology companies like Amazon, Google, Microsoft, Facebook etc are investing millions and millions of Dollars into Computer Vision based research and product development. Today, we are inundated with data of all kinds, but the plethora of photo and video data available provides the data set required to make facial recognition technology work. Facial recognition systems analyze the visual data and millions of images and videos created by high-quality Closed-Circuit Television (CCTV) cameras installed in our cities for security, smartphones, social media, and other online activity. Machine learning and artificial intelligence capabilities in the software map distinguishable facial features mathematically, look for patterns in the visual data, and compare new images and videos to other data stored in facial recognition databases to determine identity. A Facial recognition system is a technology capable of identifying or verifying a person from a digital image. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from given image with faces within a database. It is also described as a Bio-metric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. One of the major advantages of facial recognition technology is safety and security. Law enforcement agencies use the technology to uncover criminals or to find missing children or seniors. Airports are increasingly adding facial recognition technology to security checkpoints; the U.S. Department of Homeland Security predicts that it will be used on 97 percent of travelers by 2023. When people know they are being watched, they are less likely to commit crimes so the possibility of facial recognition technology being used could deter crime. Facial recognition can add conveniences. In addition to helping you tag photos in Facebook or your cloud storage via Apple and Google, you will start to be able to check-out at stores without pulling out money or credit cards—your face will be scanned. At the A.I. Bar, facial recognition technology is used to add patrons who approach the bar to a running queue to get served their drinks more efficiently. Along with all it benefits Computer vision Industry is $20 Billion industry which will be one of the most important job markets in the years to come. As the fastest growing language in popularity, Python is well suited to leverage the power of existing computer vision libraries to learn from all this image and video data. So.. Learning and mastering this Face Recognition Python technology is surely up-market and it will make you proficient in competing with the swiftly changing Image Processing technology arena. In this course we'll teach you everything you how create a Face Recognition System which can be automated so it can add images to its data set with help of user whenever new faces are detected . Here are the major topics that we are going to cover in this course. Session 1: Introduction Introduction and requirements of the course. Session 2: Basics of Computer Vision And OpenCv Students will have a basic understanding of computer vision and students will be able to Image Analysis and Manipulation using OpenCv. Session 3: Introduction to Understanding Face Recognition using face_recognition library Students will understand how face recognition works and how to implement various functions of face_recognition Library and will learn how to compare two faces using Euclidean Distance. Session 4: Project: Automated Multiple Face Detection Students will be able to understand and implement Automated Multiple Face detection AI Session 5:Future Scope and Face Recognition Market Students will understand various applications of face detection and will learn about trends in this market At the end of the course you will be able to Create Automated Multiple Face Detection System Learn Basics of Open CV Use Google Collab Understand how face recognition works Understand What is computer vision and how it works So without wasting much time, lets dive in to this magical world. See you soon in the class room. | https://www.udemy.com/course/automated-multiple-face-recognition-ai-using-python/#instructor-1 | Hello there, I am a Software Engineer and have been learning Python, Java, Android Development , Full stack Web Development and even Machine Learning, Deep Learning, Neural Networks since a long time. After gaining years of knowledge in these fields, I decided to share that knowledge with you and help you succeed with your career goals. According to me just knowledge of various languages and courses is not enough but application of them is much more important as it is truly said "Knowledge is Power But Knowledge without Application is Useless", So in My courses i will teach how to understand and create various real life applications and thus developing a thinking process to tackle difficulties. I believe that the knowledge I share will not go in vain. My primary motive is to share my expertise and provide others with the most sufficient knowledge that they can use and excel in their life and be proud of themselves. I believe in Learning Today..Leading Tomorrow! So for me, Every Student Matters..Every Moment Counts! Feel free to reach out to me with any questions you have regarding my Courses or any questions for that matter. Happy Learning 🙂 | Python | Engineer/Developer | >=2 | Below 1K | >=15K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Data Science for Professionals | It's time to leave spreadsheets behind... | 4.3 | 227 | 6843 | Created by Gregory Sward | Nov-18 | English | $9.99 | 6h 31m total length | https://www.udemy.com/course/data-science-for-professionals/ | Gregory Sward | Programmer | 4.5 | 516 | 10486 | What is it? Data Science for Professionals is simply the best way to gain a in-depth and practical skill set in data science. Through a combination of theory and hands-on practice, course participants will gain a solid grasp of how to manage, manipulate, and visualize data in R - the world's most popular data science language. Who should take this course? This course is for professionals who are tired of using spreadsheets for analysis and have a serious interest in learning how to use code to improve the quality and efficiency of their work. At the end of this course, participants will have a developed a solid foundation of the fundamentals of the R language. Participants will have also gained a perspective on the modern data science landscape and how they can use R not only to better analyze data, but also to better manage projects, create interactive presentations, and collaborate with other teams. Whether it's spreadsheets, text documents, or slides, anyone who analyzes, reports, or presents data will benefit from a knowledge of data science programming. Who should NOT take this course? While this course covers examples of machine learning in later lectures, this is not a machine learning or a statistics-focused course. The course does go through examples of how to use code to deploy and assess different types of models, including machine learning algorithms, but it does so from a coding perspective and not a statistics perspective. The reason is that the math behind most machine learning algorithms merits a course entirely on its own. There are many courses out there that make dubious claims of easy mastery of machine learning and deep learning algorithms - this is not one of those courses. A Different kind of data science course This course is different from most other courses in several ways: We use very large, real-world examples to guide our learning process. This allows us to tie-together the various aspects of data science in a more intuitive, easy-to-retain manner. We encounter and deal-with various challenges and bugs that arise from imperfect data. Most courses use ideal datasets in their examples, but these are not common in the real-world, and solving data-related issues is usually the most difficult and time-consuming part of data science. We are focused on your long-term success. Our downloadable course code is filled with notes and guidance aimed at making the transition from learning-to-applying as smooth as possible. | https://www.udemy.com/course/data-science-for-professionals/#instructor-1 | Greg has been coding in R for over 12 years (way before the language was cool). He is the co-founder and Chief Data Scientist at Occam Industries, a fin-tech startup aimed at using data science to develop next-generation financial and economic models. In his spare time, Greg creates and runs courses on R programming for The Weekend Data Course. His courses have been featured on IBM's Cognitive Class, and at colleges and universities. Greg has an Honours Bachelor of Science from the University of Ottawa, a Master of Financial Economics from the University of Toronto, and is a CFA charterholder. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
2022 Python and Machine Learning in Financial Analysis | Using Python and machine learning in financial analysis with step-by-step coding (with all codes) | 4.9 | 226 | 36585 | Created by Dr. Emadedin Hashemi | Aug-22 | English | $9.99 | 20h 17m total length | https://www.udemy.com/course/python-and-machine-learning-in-financial-analysis/ | Dr. Emadedin Hashemi | Data Scientist | 4.9 | 226 | 36582 | In this course, you will become familiar with a variety of up-to-date financial analysis content, as well as algorithms techniques of machine learning in the Python environment, where you can perform highly specialized financial analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will get acquainted with technical and fundamental analysis and you will use different tools for your analysis. You will learn the Python environment completely. You will also learn deep learning algorithms and artificial neural networks that can greatly enhance your financial analysis skills and expertise. This tutorial begins by exploring various ways of downloading financial data and preparing it for modeling. We check the basic statistical properties of asset prices and returns, and investigate the existence of so-called stylized facts. We then calculate popular indicators used in technical analysis (such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI)) and backtest automatic trading strategies built on their basis. The next section introduces time series analysis and explores popular models such as exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (including multivariate specifications). We also introduce you to factor models, including the famous Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We end this section by demonstrating different ways to optimize asset allocation, and we use Monte Carlo simulations for tasks such as calculating the price of American options or estimating the Value at Risk (VaR). In the last part of the course, we carry out an entire data science project in the financial domain. We approach credit card fraud/default problems using advanced classifiers such as random forest, XGBoost, LightGBM, stacked models, and many more. We also tune the hyperparameters of the models (including Bayesian optimization) and handle class imbalance. We conclude the book by demonstrating how deep learning (using PyTorch) can solve numerous financial problems. | https://www.udemy.com/course/python-and-machine-learning-in-financial-analysis/#instructor-1 | He is a researcher and lecturer in data science and machine learning courses. After graduating from university, he has worked and researched in the field of data science and data analysis for many years. He has collaborated with various companies in the field of financial analysis and data analysis. Has held various courses in this field that can help you improve and accelerate your performance. | Machine Learning | Data Scientist | >=4 | Below 1K | >=35K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
The Data Bootcamp: Transform your Data using dbt™ | Learn dbt™ from scratch. Build data models, perform testing, generate documents & become an all rounded Data Engineer! | 4.4 | 224 | 1242 | Created by Vantage Data | Jun-22 | English | $9.99 | 4h 7m total length | https://www.udemy.com/course/the-dbt-bootcamp-transform-your-data-using-data-build-tool/ | Vantage Data | Learn with the team @ Vantage Data | 4.4 | 224 | 1242 | Are you looking for a cutting-edge way to extract load and transform your data? Do you want to know more about dbt™ and how to use it? Well, this is the course for you. Welcome to The dbt™ Bootcamp: Transform your Data using dbt™. In this course you are going to learn all about dbt™, from setting up dbt™ cloud, connecting it to Snowflake or a warehouse of your choice, developing models, creating sources, doing testing, working with the documentation and much more. This course is for beginners, we will go through a realistic project and cover each of the steps mentioned in a practical approach. dbt™ is a data modelling tool that makes life much easier for analysts and engineers. It allows you to write SQL queries without having to worry about dependencies. dbt™, like traditional databases, is built on SQL, but it has additional functionality built on top of it utilizing templating engines such as JINJA. This effectively lets you to retrieve, rearrange, and organize your data using additional logic in your SQL. You may then compile and run this code using dbt's™ run command to retrieve just the pieces you need in the transformations. It can also be swiftly coded, tested, and adjusted without having to wait for it to process all your data. In addition to that, it’s automated documentation is a big time saver. The project we will be working on is about a fictitious company called GlobalMart. GlobalMart sells household items like furniture, office equipment, Appliances and Electronics. They are in the process of hiring a small data team and would like to try out dbt™ for their data transformations. They require some reporting tables about their profits and want to use dbt™ to transform their data to get them what they want. By the end of this course, we will work through the project and end up accomplishing the following: 1. Setting up a dbt™ Cloud Account 2. Connecting to a Database (in this case Snowflake) 3. Connecting dbt™ to a repository like GitHub 4. Understanding the dbt™ cloud interface 5. Building and Running Models in dbt™ 6. Using Modularity in dbt™ 7. Creating and Referencing Sources 8. Performing Tests in dbt™ including Singular and Generic Tests 9. How to Create and Generate Documentation in dbt™ 10. How to Deploy in dbt™ 11. How to use Jinja 12. Using Macros and Packages in dbt™ 13. Using Seeds and Analyses in dbt™ This is a great, comprehensive which will really up-skill you not only in dbt but the extract, load and transform process as well. Thank you so much for choosing this course and I’ll see you in the next lecture. | https://www.udemy.com/course/the-dbt-bootcamp-transform-your-data-using-data-build-tool/#instructor-1 | Hey, we're the team at Vantage Data. We are a group of data specialists who have decided to create fun, practical and comprehensive courses about currents tools, platforms and technology in the data space. Other than being data nerds, we have a platform called Vantage Point which is a no-code, click & go business acceleration tool which enables data driven decisions across businesses all over the world. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Applied Machine Learning in R | Get the essential machine learning skills and use them in real life situations | 4.7 | 224 | 23668 | Created by Bogdan Anastasiei | Dec-20 | English | $11.99 | 8h 10m total length | https://www.udemy.com/course/applied-machine-learning-in-r/ | Bogdan Anastasiei | University Teacher and Consultant | 4.5 | 7750 | 296607 | This course offers you practical training in machine learning, using the R program. At the end of the course you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from your data. All the machine learning procedures are explained live, in detail, on real life data sets. So you will advance fast and be able to apply your knowledge immediately – no need for painful trial-and-error to figure out how to implement this or that technique in R. Within a short time you can have a solid expertise in machine learning. Machine learning skills are very valuable if you intent to secure a job like data analyst, data scientist, researcher or even software engineer. So it may be the right time for you to enroll in this course and start building your machine learning competences today! Let’s see what you are going to learn here. First of all, we are going to discuss some essential concepts that you must absolutely know before performing machine learning. So we’ll talk about supervised and unsupervised machine learning techniques, about the distinctions between prediction and inference, about the regression and classification models and, above all, about the bias-variance trade-off, a crucial issue in machine learning. Next we’ll learn about cross-validation. This is an all-important topic, because in machine learning we must be able to test and validate our model on independent data sets (also called first seen data). So we are going to present the advantages and disadvantages of three cross-validations approaches. After the first two introductory sections, we will get to study the supervised machine learning techniques. We’ll start with the regression techniques, where the response variable is quantitative. And no, we are not going to stick to the classical OLS regression that you probably know already. We will study sophisticated regression techniques like stepwise regression (forward and backward), penalized regression (ridge and lasso) and partial least squares regression. And of course, we’ll demonstrate all of them in R, using actual data sets. Afterwards we’ll go to the classification techniques, very useful when we have to predict a categorical variable. Here we’ll study the logistic regression (classical and lasso), discriminant analysis (linear and quadratic), naïve Bayes technique, K nearest neighbor, support vector machine, decision trees and neural networks. For each technique above, the presentation is structured as follows: * a short, easy to understand theoretical introduction (without complex mathematics) * how to train the predictive model in R * how to test the model to make sure that it does a good prediction job on independent data sets. In the last sections we’ll study two unsupervised machine learning techniques: principal component analysis and cluster analysis. They are powerful data mining techniques that allow you to detect patterns in your data or variables. For each technique, a number of practical exercises are proposed. By doing these exercises you’ll actually apply in practice what you have learned. This course is your opportunity to become a machine learning expert in a few weeks only! With my video lectures, you will find it very easy to master the major machine learning techniques. Everything is shown live, step by step, so you can replicate any procedure at any time you need it. So click the “Enroll” button to get instant access to your machine learning course. It will surely provide you with new priceless skills. And, who knows, it could give you a tremendous career boost in the near future. See you inside! | https://www.udemy.com/course/applied-machine-learning-in-r/#instructor-1 | My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting. | Machine Learning | Consultant | >=4 | Below 1K | >=20K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Linear Regression, GLMs and GAMs with R | How to extend linear regression to specify and estimate generalized linear models and additive models. | 4.5 | 223 | 2146 | Created by Geoffrey Hubona, Ph.D. | Sep-20 | English | $9.99 | 7h 54m total length | https://www.udemy.com/course/linear-regression-glms-and-gams-with-r/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | Linear Regression, GLMs and GAMs with R demonstrates how to use R to extend the basic assumptions and constraints of linear regression to specify, model, and interpret the results of generalized linear (GLMs) and generalized additive (GAMs) models. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity. They permit the expected value of the response variable to be a smoothed (e.g. non-linear) monotonic function of the linear predictors. GLMs also relax the assumption that the response variable is normally distributed by allowing for many distributions (e.g. normal, poisson, binomial, log-linear, etc.). Generalized additive models (GAMs) are extensions of GLMs. GAMs allow for the estimation of regression coefficients that take the form of non-parametric smoothers. Nonparametric smoothers like lowess (locally weighted scatterplot smoothing) fit a smooth curve to data using localized subsets of the data. This course provides an overview of modeling GLMs and GAMs using R. GLMs, and especially GAMs, have evolved into standard statistical methodologies of considerable flexibility. The course addresses recent approaches to modeling, estimating and interpreting GAMs. The focus of the course is on modeling and interpreting GLMs and especially GAMs with R. Use of the freely available R software illustrates the practicalities of linear, generalized linear, and generalized additive models. | https://www.udemy.com/course/linear-regression-glms-and-gams-with-r/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
The Complete Intro to Machine Learning | Hands-on ML with Python, Pandas, Regression, Decision Trees, Neural Networks, and more! | 3.9 | 222 | 27411 | Created by Student ML Coalition, Michael Lutz, Arjun Rajaram, Saurav Kumar | Nov-21 | English | $9.99 | 5h 6m total length | https://www.udemy.com/course/the-complete-intro-to-machine-learning-with-python/ | Student ML Coalition | Community Organization | 3.9 | 222 | 27411 | Interested in machine learning but confused by the jargon? If so, we made this course for you. Machine learning is the fastest-growing field with constant groundbreaking research. If you're interested in any of the following, you'll be interested in ML: Self-driving cars Language processing Market prediction Self-playing games And so much more! No past knowledge is required: we'll start with the basics of Python and end with gradient-boosted decision trees and neural networks. The course will walk you through the fundamentals of machine learning, explaining mathematical foundations as well as practical implementations. By the end of our course, you'll have worked with five public data sets and have implemented all essential supervised learning models. After the course's completion, you'll be equipped to apply your skills to Kaggle data science competitions, business intelligence applications, and research projects. We made the course quick, simple, and thorough. We know you're busy, so our curriculum cuts to the chase with every lecture. If you're interested in the field, this is a great course to start with. Here are some of the Python libraries you'll be using: Numpy (linear algebra) Pandas (data manipulation) Seaborn (data visualization) Scikit-learn (optimized machine learning models) Keras (neural networks) XGBoost (gradient-boosted decision trees) Here are the most important ML models you'll use: Linear Regression Logistic Regression Random Forrest Decision Trees Gradient-Boosted Decision Trees Neural Networks Not convinced yet? By taking our course, you'll also have access to sample code for all major supervised machine learning models. Use them how you please! Start your data science journey today with The Complete Intro to Machine Learning with Python. | https://www.udemy.com/course/the-complete-intro-to-machine-learning-with-python/#instructor-1 | The Student Machine Learning Coalition is a global student-led organization that aims to make ML more accessible. Comprised of college and high school students, we provide a platform for students to join workshops, engage in Kaggle Competitions, and receive guidance on projects—all for free. SMLC chapters are in countries around the world (from the US to India to Nepal), and we comprise roughly 400 students worldwide. As an organization, our members have already placed in the top 1% and top 5% in multiple Kaggle data science competitions with 20,000+ attendees. Moreover, students have qualified for silver and bronze medals in competitions sponsored by companies and organizations such as Lyft and Harvard University’s Laboratory of Innovation Science. Through Udemy, we hope to make our resources accessible to anyone that might be interested. | Machine Learning | >=3 | Below 1K | >=25K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
R for Data Science: Learn R Programming in 2 Hours | Enter the world of R Programming: Everything you need to get started with R and Data Science in just 2 HOURS! | 4.5 | 216 | 3830 | Created by Ajay R Warrier | Feb-22 | English | $9.99 | 1h 52m total length | https://www.udemy.com/course/r-for-data-science-learn-r-programming-in-2-hours/ | Ajay R Warrier | Making Your Life Easier. | 4.5 | 1597 | 46359 | BEST R BEGINNERS COURSE ON UDEMY! LEARN R PROGRAMMING IN 2 HOURS! This course will not waste your time, Are you tired of watching tutorials that take hours to explain simple concepts? You came to the right place. All this course asks you is 2-3 hours of your life. Are you tired of taking countless courses on Udemy that are 10+ hours and you leave it halfway because they're too darn long? You've come to the right place. R for Data Science; LEARN R PROGRAMMING IN 2 HOURS? Sounds a bit too good to be true? No. This is the class I wish I had when I was trying to learn R Programming. I have a unique way of teaching, as I know how it must be overwhelming to learn a very complex programming language. The best part of this course is No prior programming experience is required. 2-3 hours is all you need to learn the basics of any programming language. Don't believe me? just go check the reviews on my other courses. I've been teaching programming on Udemy for the last 4 years with similar short courses and my students love them. So spend the next 2 hours that you would normally waste on a random Youtube video on this course and get maximum value in the minimum amount of time. This course will introduce you to the concepts of R programming and Data Science in just 2 Hours. Here's What People Are Saying About My Programming Courses: "Excellent Course. Worth every Dollar. I always wanted to learn python. A few months back I purchased Ajay's C++ course and I loved it. I was excited to see him release a course on python. The course doesn't deviate from the topic like most courses on Python. This course didn't disappoint at all. I am only halfway in the course, but I am still able to write small programs. Downloadable lecture notes make the learning process a lot easier. If you are a beginner like me and want to write fun programs on Python fast, look no further and enroll this course" "Perfect Course for Beginners at Wonderful Price. Well, I was a little concerned about enrolling in this course as it was just released, but I have to say it beats all the other C++ Courses in the market. The best part is that it’s just 2 hours, the content is straight forward and doesn't waste your time just as it’s said in the promo video. Worth every buck! Will recommend it to all the beginners." "Very Good Course for Beginners This course covers all the basic concepts of C++ in an easily understandable and interactive way. The instructor Ajay is also very helpful and replies readily to your queries and doubts. Overall I would strongly recommend this course to you if you are looking for basic knowledge of C++." "Excellent Course I really enjoyed taking this course. I would definitely recommend this course to anyone with an interest in C++. It covers all the basics and good tips are given during the course. Ajay certainly knows the subject he teaches here. Looking forward to his next course." "Good primer I'm brand new to Python, so this course was really just what I needed. I would like it to have been a bit longer and go a bit deeper, but as a brand new Python coder, I really enjoyed it and learned the basics." SO WHAT ARE YOU WAITING FOR? ENROLL NOW AND LET'S GET STARTED, | https://www.udemy.com/course/r-for-data-science-learn-r-programming-in-2-hours/#instructor-1 | Ajay Warrier is the founder of Bananas Academy, an independent game studio that makes educational games. He also teaches programming to more than 45000 students from all over the world on Udemy. A Computer Science Engineer with a Master's Degree in Marketing. He has industry-level experience in game development(Godot), cross-platform mobile development (Flutter), and distributed applications (Ethereum Blockchain). | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Outlier Detection Algorithms in Data Mining and Data Science | Outlier Detection in Data Mining, Data Science, Machine Learning, Data Analysis and Statistics using PYTHON,R and SAS | 4.3 | 209 | 2170 | Created by KDD Expert | Jan-19 | English | $9.99 | 2h 16m total length | https://www.udemy.com/course/outlier-detection-techniques/ | KDD Expert | Data Scientist | 4.4 | 320 | 2941 | Welcome to the course " Outlier Detection Techniques ". Are you Data Scientist or Analyst or maybe you are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities? Welcome to Outlier Detection Techniques, a course designed to teach you not only how to recognise various techniques but also how to implement them correctly. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you’ll learn have implementation in PYTHON, R and SAS. So what do you need to know before you get started? In short, not much! This course is perfect even for those with no knowledge of statistics and linear algebra. Why wait? Start learning today! Because Everyone, who deals with the data, needs to know "Outlier Detection Techniques"! The process of identifying outliers has many names in Data Mining and Machine learning such as outlier mining, outlier modeling, novelty detection or anomaly detection. Outlier detection algorithms are useful in areas such as: Data Mining, Machine Learning, Data Science, Pattern Recognition, Data Cleansing, Data Warehousing, Data Analysis, and Statistics. I will present you on the one hand, very popular algorithms used in industry, but on the other hand, i will introduce you also new and advanced methods developed in recent years, coming from Data Mining. You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn innovative algorithm for detection outliers in High-dimensional space. I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation, can understand it in depth. So, in my teaching method, I put a stronger emphasis on understanding the material, and less on programming. However, anyone who interested in programming, I developed all algorithms in R , Python and SAS, so you can download and run them. List of Algorithms: Univariate space: 1. Three Sigma Rule ( Statistics , R + Python + SAS programming languages) 2. MAD ( Statistics , R + Python + SAS programming languages ) 3. Boxplot Rule ( Statistics , R + Python + SAS programming languages ) 4. Adjusted Boxplot Rule ( Statistics , R + Python + SAS programming languages ) Low-dimensional Space : 5. Mahalanobis Rule ( Statistics , R + Python + SAS programming languages ) 6. LOF - Local Outlier Factor ( Data Mining , R + Python + SAS programming languages) High-dimensional Space: 7. ABOD - Angle-Based Outlier Detection ( Data Mining , R + Python + SAS programming languages) I sincerely hope you will enjoy the course. | https://www.udemy.com/course/outlier-detection-techniques/#instructor-1 | I am Data Scientist. I have more than 10 years experience working with analyzing of data, Data Mining and Machine Learning. Data Analysis it's more than my job, it's my hobby. I love it and enjoy it. I am programmer in Python, SAS, R and Matlab. I am interested and developing models for retail market, such as product assortment decisions, predicting customer loyalty, demand forecasting and et cetera. In addition, i developed and implemented a variety of models in the area of risk management and finance. | Misc | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Practical Neural Networks & Deep Learning In R | Artificial Intelligence & Machine Learning for Practical Data Science in R | 4.4 | 208 | 1737 | Created by Minerva Singh | Oct-21 | English | $9.99 | 5h 36m total length | https://www.udemy.com/course/practical-neural-networks-deep-learning-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | YOUR COMPLETE GUIDE TO PRACTICAL NEURAL NETWORKS & DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level! LEARN FROM AN EXPERT DATA SCIENTIST: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R. Among other things: You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and recurrent neural networks (RNN). You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications. With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom! NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life. After taking this course, you’ll easily use data science packages like caret, h2o, mxnet to work with real data in R... You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data. We will also work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW! | https://www.udemy.com/course/practical-neural-networks-deep-learning-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Deep Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Python Regression Analysis: Statistics & Machine Learning | Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in Python | 4.4 | 207 | 1968 | Created by Minerva Singh | Nov-22 | English | $11.99 | 6h 25m total length | https://www.udemy.com/course/python-regression-analysis-statistics-machine-learning/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in Python in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions...All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course is Different; It will help you go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. THIS COURSE WILL HELP YOU BECOME A REGRESSION ANALYSIS EXPERT:Here is what we'll be covering inside the course:Get started with Python and Anaconda. Install these on your system, learn to load packages and read in different types of data in PythonCarry out data cleaning PythonImplement ordinary least square (OLS) regression in Python and learn how to interpret the results.Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression using PythonUse machine learning based regression techniques for predictive modelling Work with tree-based machine learning modelsImplement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.& Carry out model selectionTHIS IS A PRACTICAL GUIDE TO REGRESSION ANALYSIS WITH REAL LIFE DATA:This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will: (a) Take you from a basic level of statistical knowledge to performing some of the most common advanced regression analysis based techniques. (b) Equip you to use Python for performing the different statistical and machine learning data analysis tasks. (c) Introduce some of the most important statistical and machine learning concepts to you in a practical manner so you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) You will be able to decide which regression analysis techniques are best suited to answer your research questions and applicable to your data and interpret the results.It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis... However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. JOIN THE COURSE NOW! | https://www.udemy.com/course/python-regression-analysis-statistics-machine-learning/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Machine Learning | Data Scientist | Yes | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||
YOLOv3 - Robust Deep Learning Object Detection in 1 hour | The Complete Guide to Creating your own Custom AI Object Detection. Learn the Full Workflow - From Training to Inference | 4.2 | 207 | 1344 | Created by Augmented Startups | May-20 | English | $9.99 | 2h 18m total length | https://www.udemy.com/course/yolo-v3-robust-deep-learning-object-detection-in-1-hour/ | Augmented Startups | M(Eng) AI Instructor 97k+ Subs on YouTube & 60k+ students | 3.8 | 3532 | 56203 | Learn how we implemented YOLO V3 Deep Learning Object Detection Models From Training to Inference - Step-by-Step When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is that if you are just getting started learning about AI Object Detection, you may encounter some of the following common obstacles along the way: Labeling dataset is quite tedious and cumbersome, Annotation formats between various object detection models are quite different. Labels may get corrupt with free annotation tools, Unclear instructions on how to train models - causes a lot of wasted time during trial and error. Duplicate images are a headache to manage. This got us searching for a better way to manage the object detection workflow, that will not only help us better manage the object detection process but will also improve our time to market. Amongst the possible solutions we arrived at using Supervisely which is free Object Detection Workflow Tool, that can help you: Use AI to annotate your dataset, Annotation for one dataset can be used for other models (No need for any conversion) - Yolo, SSD, FR-CNN, Inception etc, Robust and Fast Annotation and Data Augmentation, Supervisely handles duplicate images. You can Train your AI Models Online (for free) from anywhere in the world, once you've set up your Deep Learning Cluster. So as you can see, that the features mentioned above can save you a tremendous amount of time. In this course, I show you how to use this workflow by training your own custom YoloV3 as well as how to deploy your models using PyTorch. So essentially, we've structured this training to reduce debugging, speed up your time to market and get you results sooner. In this course, here's some of the things that you will learn: Learn the State of the Art in Object Detection using Yolo V3 pre-trained model, Discover the Object Detection Workflow that saves you time and money, The quickest way to gather images and annotate your dataset while avoiding duplicates, Secret tip to multiply your data using Data Augmentation, How to use AI to label your dataset for you, Find out how to train your own custom YoloV3 from scratch, Step-by-step instructions on how to Execute,Collect Images, Annotate, Train and Deploy Custom Yolo V3 models, and much more... You also get helpful bonuses: Neural Network Fundamentals Personal help within the course I donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and I will do my best to help you. The office hours are free. I don't try to sell anything. Students can start discussions and message me with private questions. I answer 99% of questions within 24 hours. I love helping students who take my courses and I look forward to helping you. I regularly update this course to reflect the current marketing landscape. Get a Career Boost with a Certificate of Completion Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction. If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs. Money-Back Guarantee The course comes with an unconditional, Udemy-backed, 30-day money-back guarantee. This is not just a guarantee, it's my personal promise to you that I will go out of my way to help you succeed just like I've done for thousands of my other students. Let me help you get fast results. Enroll now, by clicking the button and let us show you how to Develop Object Detection Using Yolo V3. | https://www.udemy.com/course/yolo-v3-robust-deep-learning-object-detection-in-1-hour/#instructor-1 | So a bit about me, Ritesh Kanjee: I've graduated from University of Johannesburg as an Electronic Engineer with a Masters in Image Processing and 8 years ago I started my online school called Augmented Startups where I have over 97'000 subscribers on YouTube and over 60'000 students on Augmented AI Bootcamp/Udemy. I’ve worked with popular tools such as TensorFlow Keras, Open CV, and PyTorch and I’ve also produced High ranking tutorials that feature on Google and YouTube. My Machine Learning Series is also one of the most viewed videos, over 300 thousand views and you’ll find them ranked right at the top on YouTube search results. From my tutorials, I have received a lot of great feedback and testimonials from students all around the world, I will share those reviews towards the end of the video And I have also presented at international conferences and meetups in AI. For industry standard AI, I have partnered up with Geeky Bee AI who are Experts in the field in AI and Deep Learning and have experience developing AI apps for real world applications. | Deep Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Machine Learning: Build neural networks in 77 lines of code | Machine Learning and Artificial Intelligence for beginners. How to build a neural network in 77 lines of Python code. | 4.3 | 205 | 706 | Created by Milo Spencer-Harper | Jan-19 | English | $9.99 | 56m total length | https://www.udemy.com/course/machine-learning-build-a-neural-network-in-77-lines-of-code/ | Milo Spencer-Harper | Software Engineer | 4.5 | 348 | 1117 | From Google Translate to Netflix recommendations, neural networks are increasingly being used in our everyday lives. One day neural networks may operate self driving cars or even reach the level of artificial consciousness. As the machine learning revolution grows, demand for machine learning engineers grows with it. Machine learning is a lucrative field to develop your career. In this course, I will teach you how to build a neural network from scratch in 77 lines of Python code. Unlike other courses, we won't be using machine learning libraries, which means you will gain a unique level of insight into how neural networks actually work. This course is designed for beginners. I don't use complex mathematics and I explain the Python code line by line, so the concepts are explained clearly and simply. This is the expanded and improved video version of my blog post "How to build a neural network in 9 lines of Python code" which has been read by over 500,0000 students. Enroll today to start building your neural network. | https://www.udemy.com/course/machine-learning-build-a-neural-network-in-77-lines-of-code/#instructor-1 | After studying at Oxford University, I was struck by how the best professors made very complex ideas easy to understand. That is my mission. I believe in bringing the breakthroughs occurring in artificial intelligence from inaccessible academic papers to anyone who wants to learn. Over 600,000 students have read my blog post "How to build a neural network in 9 lines of Python code". I'm excited to bring courses on machine learning and artificial intelligence to Udemy. | Machine Learning | Engineer/Developer | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Time Series Analysis and Forecasting with Python | Learn Python for Pandas, Statsmodels, ARIMA, SARIMAX, Deep Learning, LSTM and Forecasting into Future | 4.6 | 205 | 3995 | Created by Navid Shirzadi | Jan-22 | English | $9.99 | 10h 18m total length | https://www.udemy.com/course/time-series-analysis-and-forecasting-with-python/ | Navid Shirzadi | Data Analyst - Optimization Expert | 4.5 | 1523 | 71220 | "Time Series Analysis and Forecasting with Python" Course is an ultimate source for learning the concepts of Time Series and forecast into the future. In this course, the most famous methods such as statistical methods (ARIMA and SARIMAX) and Deep Learning Method (LSTM) are explained in detail. Furthermore, several Real World projects are developed in a Python environment and have been explained line by line! If you are a researcher, a student, a programmer, or a data science enthusiast that is seeking a course that shows you all about time series and prediction from A-Z, you are in a right place. Just check out what you will learn in this course below: Basic libraries (NumPy, Pandas, Matplotlib) How to use Pandas library to create DateTime index and how to set that as your Dataset index What are statistical models? How to forecast into future using the ARIMA model? How to capture the seasonality using the SARIMAX model? How to use endogenous variables and predict into future? What is Deep Learning (Very Basic Concepts) All about Artificial and Recurrent Neural Network! How the LSTM method Works! How to develop an LSTM model with a single variate? How to develop an LSTM model using multiple variables (Multivariate) As I mentioned above, in this course we tried to explain how you can develop an LSTM model when you have several predictors (variables) for the first time and you can use that for several applications and use the source code for your project as well! This course is for Everyone! yes everyone! that wants t to learn time-series and forecasting into the future using statistics and artificial intelligence with any kind of background! Even if you are not a programmer, I show you how to code and develop your model line by line! If you want to master the basics of Machine Learning in Python as well, you can check my other courses! | https://www.udemy.com/course/time-series-analysis-and-forecasting-with-python/#instructor-1 | My name is Navid Shirzaid and I am super excited that you are here to read this section! I am a researcher with more than 7 years of experience in the field of controlling integrated energy systems with extensive skill in using mathematical optimization strategies. I am also proficient in coding with Python and developing machine learning and deep learning models for different applications. I have several publications in the field of designing and control strategies of energy systems using machine learning, deep learning, and artificial intelligence. To Conclude, I am passionate about Data Science and Machine Learning, and Optimization applications in real-world problems and I really like to share my experience with you! | Python | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
40 Real World Data Science, Machine Learning Projects 2022 | Learn To Build & Deploy AI, ML, DS, Deep Learning, NLP Web Apps With Python Projects Course(Flask, Django, Heruko Cloud) | 4.3 | 204 | 6733 | Created by Pianalytix . | Oct-21 | English | $9.99 | 29h 23m total length | https://www.udemy.com/course/intro-to-machine-learning-course/ | Pianalytix . | Technology For Innovators | 4.6 | 1350 | 65362 | Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Artificial Intelligence, Machine Learning, Data Science , Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud). How much does a Data Scientist make in the United States? The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor) Salaries by Company, Role, Average Base Salary in (USD) Facebook Data Scientist makes US$1,36,000/yr. Analyzed from 1,014 salaries. Amazon Data Scientist makes US$1,25,704/yr. Analyzed from 307 salaries. Apple Data Scientist makes US$1,53,885/yr. Analyzed from 147 salaries. Google Data Scientist makes US$1,48,316/yr. Analyzed from 252 salaries. Quora, Inc. Data Scientist makes US$1,22,875/yr. Analyzed from 509 salaries. Oracle Data Scientist makes US$1,48,396/yr. Analyzed from 458 salaries. IBM Data Scientist makes US$1,32,662/yr. Analyzed from 388 salaries. Microsoft Data Scientist makes US$1,33,810/yr. Analyzed from 205 salaries. Walmart Data Scientist makes US$1,08,937/yr. Analyzed 187 salaries. Cisco Systems Data Scientist makes US$1,57,228/yr. Analyzed from 184 salaries. Uber Data Scientist makes US$1,43,661/yr. Analyzed from 151 salaries. Intel Corporation Data Scientist makes US$1,25,930/yr. Analyzed from 131 salaries. Airbnb Data Scientist makes US$1,80,569/yr. Analyzed from 122 salaries. Adobe Data Scientist makes US$1,39,074/yr. Analyzed from 109 salaries. In This Course, We Are Going To Work On 40 Real World Projects Listed Below: Project-1 Pan Card Tempering Detector App (With Deployment) Project-2 Dog breed prediction Flask App Project-3 Image Watermarking App (With Deployment) Project-4 Traffic sign classification Project-5 Text Extraction From Images App Project-6 Plant Disease Prediction App (With Deployment) Project-7 Counting & Detecting Vehicles Flask App Project-8 Face Swapping Deep Learning Application Project-9 Bird Species Prediction App Project-10 Intel Image Classification App Project-11 Sentiment Analysis App (With Deployment) Project-12 Attrition Rate Django App Project-13 Pokemon Dataset App (With Deployment) Project-14 Face Detection App Streamlit Project-15 Cats Vs Dogs Classification App Project-16 Customer Revenue Prediction App (With Deployment) Project-17 Gender From Voice Prediction App Project-18 Restaurant Recommendation System Project-19 Happiness Ranking App Project-20 Forest Fire Prediction App Project-21 Black Friday Sale Prediction Project-22 Sentiment Analysis Using Natural Language Processing Project-23 Parkinson Syndrome Prediction Project-24 Fake News Classifier Using NLP Project-25 Toxic Comment Classifier Using NLP Project-26 Movie Ratings Prediction (IMDB) Using NLP Project-27 Air Quality Prediction Project-28 Covid-19 Case Analysis Project-29 Customer Churning Prediction Project-30 Building A Chatbot Application (NLP) Project-31: Video Game Sales Prediction App Project-32: Car Selling Price Prediction App- Deploy On Heruku Project-33: Affair Prediction App - Deploy On Heruku Project-34: Mushroom Classification App - Deploy On Heruku Project-35: Mobile App Rating Prediction Django App - Deploy On Heruku Project-36: Heart Attack Risk Prediction With Auto ML Project-37: Credit Card Fraud Detection using PyCaret Project-38: Flight Fare Detection using Auto SK Learn Project-39: Petrol Price Forecasting using Auto Keras Project-40: Bank Customer Churn Prediction using H2O Auto ML The Only Course With 40 Real World Projects In Data Science Domain. Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires. | https://www.udemy.com/course/intro-to-machine-learning-course/#instructor-1 | Pianalytix Edutech Pvt Ltd uses cutting-edge AI technology & innovative product design to help users learn Machine Learning more efficiently and to implement Machine Learning in the real world. Pianalytix also leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Deep Reinforcement Learning: Hands-on AI Tutorial in Python | Develop Artificial Intelligence Applications using Reinforcement Learning in Python. | 4 | 204 | 16387 | Created by Mehdi Mohammadi | Oct-20 | English | $9.99 | 4h 3m total length | https://www.udemy.com/course/deep-reinforcement-learning-a-hands-on-tutorial-in-python/ | Mehdi Mohammadi | Machine Learning Engineer | 4 | 204 | 16387 | In this course we learn the concepts and fundamentals of reinforcement learning, it's relation to artificial intelligence and machine learning, and how we can formulate a problem in the context of reinforcement learning and Markov Decision Process. We cover different fundamental algorithms including Q-Learning, SARSA as well as Deep Q-Learning. We present the whole implementation of two projects from scratch with Q-learning and Deep Q-Network. | https://www.udemy.com/course/deep-reinforcement-learning-a-hands-on-tutorial-in-python/#instructor-1 | I am a senior machine learning engineer. I received my Ph.D. degree in computer science from Western Michigan University. With a background in the Internet of Things and Machine Learning, I am always passionate to combine the power of machine learning to the Internet of Things to make a positive impact on our lives and communities. I’ve contributed to this area in my research works and followed my professional career in the same direction. During my doctoral studies, I focused on the development of advanced machine learning techniques for the Internet of Things. My survey and tutorial research works on IoT and Deep Learning has attracted the attention of researchers and students worldwide. I am the recipient of the best survey paper award 2018 for "Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications", IEEE Communications Surveys & Tutorials journal. I’ve run several workshops on topics like Developing IoT applications, Deep learning, and Reinforcement learning. I have more than 10 years of software engineering experience. During these years I have worked and gained experience with a wide range of technologies including web development, .Net framework, big data platforms (Apache Hadoop, Spark), cloud computing, and hybrid mobile app development. | Python | Engineer/Developer | >=4 | Below 1K | >=15K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Time Series Analysis in Python: Master Applied Data Analysis | Python Time Series Analysis with 10+ Forecasting Models including ARIMA, SARIMA, Regression & Time Series Data Analysis | 4.5 | 203 | 15327 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 9h 37m total length | https://www.udemy.com/course/time-series-data-analysis-with-python/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | The Ultimate Course on Time Series Analysis in Python brings you expertise in Forecasting Models, Regression, ARIMA, SARIMA, and Time Series Data Analysis with Python Do you want to know how meteorologists forecast the weather? Do you want to know how retailers reduce excess inventory and increase profit margin? Predict the future using Time Series Forecasting! Time series forecasting is all about looking into the future. Time Series is an important field in statistical programming. It allows you to analyze:- 1. Trends 2. Seasonality 3. Irregularity Time Series Analysis has tons of applications such as stock market analysis, pattern recognition, earthquake prediction, census analysis, and many more. Due to the advanced modern technologies, the data is growing exponentially and this data can be used to modelled for the future which can really make a big difference. You are at the right place! Welcome to this online resource to learn Time Series Analysis using Python. This course will really help you to boost your career. This course begins with the basic level and goes up to the most advanced techniques step by step. Even if you do not know anything about time series, this course will make complete sense to you. In this course you will learn about the following:- 1. What is time-series data, its applications, and components. 2. Fetching time series data using different methods. 3. Handling missing values and outliers in time series data. 4. Decomposing and splitting time series data. 5. Different smoothing techniques such as simple moving averages, simple exponential, holt, and holt-winter exponential. 6. Checking stationarity of the time series data and converting non-stationary to stationary. 7. Auto-regressive models such as simple AR model and moving average model. 8. Advanced auto-regressive models such as ARMA, ARIMA, SARIMA. 9. ARIMAX and SARIMAX model. 10. Evaluation metrics used for time series data. 11. Rules for choosing the right model for time series data. All the mentioned topics will be covered theoretically as well as implemented in code. You will compare all the models and will see how to read the results. We will work with real data and you will have access to all the resources used in this course. Also, this course has Time Series Analysis Project where you can Understand and Analyze the Stock Market and Its Fluctuations to Forecast Stock Prices using Advanced AI Algorithms. This course is for everyone who wants to master time series and become proficient in working with real-life time-based data. For taking up this course you need to have prior knowledge of Python programming. But wait! Here is the surprise!! If you are not aware of the python programming language then also don't worry. We have a crash course in python for you. You can take up python's crash course and then proceed with the time series analysis. | https://www.udemy.com/course/time-series-data-analysis-with-python/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Python | Grandmaster | >=4 | Below 1K | >=15K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
VSD - Machine Intelligence in EDA/CAD | Listen from CEO/architect himself on Machine learning | 3.8 | 202 | 776 | Created by Kunal Ghosh, Rohit Sharma | Apr-19 | English | $9.99 | 4h 8m total length | https://www.udemy.com/course/vsd-machine-intelligence-in-eda-cad/ | Kunal Ghosh | Digital and Sign-off expert at VLSI System Design(VSD) | 4.2 | 12021 | 41594 | This webinar was conducted on 31st March 2018 with Rohit, CEO Paripath Inc. We start with Electronic design automation and what is machine learning. Then we will give overall introduction to categories of machine learning (supervised and unsupervised learning) and go about discussing that a little bit. Then we talk about the frameworks which are available today, like general purpose, big data processing and deep-learning, and which one is suitable for design automation. This is Machine Learning in general with a focus on CAD, EDA and VLSI flows. Then we talk about Applied Theory (data sets, data analysis like data augmentation, exploratory data analysis, normalization, randomization), as to what are the terms and terminologies and what do we do with that, accuracy, how do we develop the algorithm, essentially the things that are required to develop the solution flow, lets say, you as the company wants to add a feature in your product using machine learning, what you would be doing, and what your flow will look like and this is what is shown as pre-cursor of flight theory as what you should be looking out. And then we start with regression, which is first in supervised learning. In the regression, we will give couple of example, like first is resistance estimation, second is polynomial regression which is capacitance estimation. For resistance estimation, we have the dataset from 20nm technology. And finally, we go on to create a linear classifier using logistic regression. Next will be dimensionality reduction, meaning, you have a large dataset and how to you reduce the size of that so that you can run on a laptop or even on your cell phone. Then there is a big example of that. Everything has mathematics behind that, this wont be a part of the webinar. About Rohit - Rohit Sharma is Founder and CEO of Paripath Inc based in Milpitas, CA. He graduated from IIT Delhi.He has authored 2 books and published several papers in international conferences and journals. He has contributed to electronic design automation domain for over 20 years learning, improvising and designing solutions. He is passionate about many technical topics including Machine Learning, Analysis, Characterization and Modeling, which led him to architect guna - an advanced characterization software for modern nodes.He currently works for Paripath Inc. | https://www.udemy.com/course/vsd-machine-intelligence-in-eda-cad/#instructor-1 | Kunal Ghosh is the Director and co-founder of VLSI System Design (VSD) Corp. Pvt. Ltd. Prior to launching VSD in 2017, Kunal held several technical leadership positions at Qualcomm's Test-chip business unit. He joined Qualcomm in 2010. He led the Physical design and STA flow development of 28nm, 16nm test-chips. At 2013, he joined Cadence as Lead Sales Application engineer for Tempus STA tool. Kunal holds a Masters degree in Electrical Engineering from Indian Institute of Technology (IIT), Bombay, India and specialized in VLSI Design & Nanotechnology. Hands on with Technology @ 1) MSM (mobile station mode chips) - MSM chips are used for CDMA modulation/demodulation. It consists of DSP’s and microprocessors for running applications such as web-browsing, video conferencing, multimedia services, etc. 2) Memory test chips - Memory test chips are used to validate functionality of 28nm custom/compiler memory as well as characterize their timing, power and yield. 3) DDR-PHY test chips - DDR-PHY test chips are basically tested for high speed data transfer 4) Timing and physical design Flow development for 130nm MOSFET technology node till 16nm FinFET technology node. 5) “IR aware STA” and “Low power STA” 6) Analyzed STA engine behavior for design size up to 850 million instance count ACADEMIC 1) Research Assistant to Prof. Richard Pinto and Prof. Anil Kottantharayil on “Sub-100nm optimization using Electron Beam Lithography”, which intended to optimize RAITH-150TWO Electron Beam Lithography tool and the process conditions to attain minimum resolution, use the mix-and-match capabilities of the tool for sub-100nm MOSFET fabrication and generate mask plates for feature sizes above 500nm. 2) Research Assistant to with Prof. Madhav Desai, to characterize RTL, generated from C-to-RTL AHIR compiler, in terms of power, performance and area. This was done by passing RTL, generated from AHIR compiler, through standard ASIC tool chain like synthesis and place & route. The resulting netlist out of PNR was characterized using standard software PUBLICATION 1) “A C-to-RTL Flow as an Energy Efficient Alternative to Embedded Processors in Digital Systems” submitted in the conference “13th Euromicro Conference on Digital System Design, Architectures, Methods and Tools, DSD 2010, 1-3 September 2010, Lille, France” 2) Concurrent + Distributed MMMC STA for 'N' views 3) Signoff Timing and Leakage Optimization On 18M Instance Count Design With 8000 Clocks and Replicated Modules Using Master Clone Methodology With EDI Cockpit 4) Placement-aware ECO Methodology - No Slacking on Slack Tips on order in which you need to learn VLSI and become a CHAMPION: If I would had been you, I would had started with Physical Design and Physical design webinar course where I understand the entire flow first, then would have moved to CTS-1 and CTS-2 to look into details of how the clock is been built. Then, as you all know how crosstalk impacts functioning at lower nodes, I would gone for Signal Integrity course to understand impacts of scaling and fix them. Once I do that, I would want to know how to analyze performance of my design and I would have gone for STA-1, STA-2 and Timing ECO webinar courses, respectively Once you STA, there’s an internal curiosity which rises, and wants us to understand, what goes inside timing analysis at transistor level. To full-fill that, I would had taken Circuit design and SPICE simulations Part 1 and Part 2 courses. And finally, to understand pre-placed cells, IP’s and STA in even more detail, I would have taken custom layout course and Library Characterization course All of above needs to be implemented using a CAD tool and needs to be done faster, for which I would have written TCL or perl scripts. So for that, I would start to learn TCL-Part1 and TCL-Part2 courses, at very beginning or in middle Finally, if I want to learn RTL and synthesis, from specifications to layout, RISC-V ISA course will teach the best way to define specs for a complex system like microprocessor Connect with me for more guidance !! Hope you enjoy the session best of luck for future | Misc | >=3 | Below 1K | Below 1K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||||
Machine Learning Guide: Learn Machine Learning Algorithms | Machine Learning: A comprehensive guide to machine learning. Learn machine learning algorithms & machine learning tools | 2.7 | 202 | 10078 | Created by Grid Wire | May-19 | English | $9.99 | 1h 6m total length | https://www.udemy.com/course/machine-learning-algorithms/ | Grid Wire | Freelancer | 3.5 | 552 | 22830 | Artificial Intelligence is becoming progressively more relevant in today's world. The rise of AI has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Machine learning is one of the most important areas of Artificial Intelligence. Machine learning provides developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. It can be applied across many industries to increase profits, reduce costs, and improve customer experiences. In this course I'm going to provide you with a comprehensive introduction to the field of machine learning. You will learn how to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. Also i'm going to offer you a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics. You'll discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. In addition you'll learn how to drive innovation by combining data, technology and design to solve real problems at an enterprise scale. This course is focused on helping you drive concrete business decisions through applications of artificial intelligence and machine learning. It makes the fundamentals and algorithms of machine learning accessible to students in statistics, computer science, mathematics, and engineering. This means plain-English explanations and no coding experience required. This is the best practical guide for business leaders looking to get true value from the adoption of machine learning technology. | https://www.udemy.com/course/machine-learning-algorithms/#instructor-1 | Welcome to GridWire. Here you will find courses on many topics including: Data Science, Machine Learning, DevOps, Sales, Marketing and Business. About me: I have been a freelancer for almost 10 years now and I am making my living out of it. I love teaching my skills to other people and wish to get better at doing it. I enjoy sharing my knowledge and I always try to make it easy to understand to absolutely anyone. I hope you will find my courses insightful and valuable. | Machine Learning | >=2 | Below 1K | >=10K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Deep learning for object detection using Tensorflow 2 | Understand, train and evaluate Faster RCNN, SSD and YOLO v3 models using Tensorflow 2 and Google AI Platform | 4.5 | 198 | 1799 | Created by Nour Islam Mokhtari | Apr-21 | English | $9.99 | 9h 51m total length | https://www.udemy.com/course/deep-learning-for-object-detection-using-tensorflow-2/ | Nour Islam Mokhtari | Computer Vision and Machine Learning engineer | 4.4 | 385 | 4473 | This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNN, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of Tensorflow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GPUs offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including : Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning. By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions. By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GPUs. | https://www.udemy.com/course/deep-learning-for-object-detection-using-tensorflow-2/#instructor-1 | My name is Nour-Islam Mokhtari and I am a machine learning engineer with a focus on computer vision applications. I have 3 years of experience developing and maintaining deep learning pipelines. I worked on several artificial intelligence projects, mostly focused on applying deep learning research to real world industry projects. My goal on Udemy is to help my students learn and acquire real world and industry focused experience. I aim to build courses that can make your learning experience smooth and focused on the practical aspects of things! | Deep Learning | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Build and train a data model to recognize objects in images! | Make an image recognition model with TensorFlow & Python predictive modeling, regression analysis & machine learning! | 4 | 197 | 1429 | Created by Mammoth Interactive, John Bura | Jan-19 | English | $9.99 | 8h 25m total length | https://www.udemy.com/course/pythondatascience/ | Mammoth Interactive | Top-Rated Instructor, 800,000+ Students | 4.3 | 11870 | 0 | "Well done!!!!!! I found it the BEST source for me out of many to learn how to implement AI project due the facts it starts from the very basics of Python and TensorFlow and assumes no prior knowledge (or almost no prior knowledge) which should not be taken for granted since other courses do so. The instructor is wonderful and explains all the concepts wonderfully! Thank you so much! helped me a lot!" "Very easy to understand. Loving it so far!" - Arthur G. This course was funded by a wildly successful Kickstarter. Let's learn how to perform automated image recognition! In this course, you learn how to code in Python, calculate linear regression with TensorFlow, and perform CIFAR 10 image data and recognition. We interweave theory with practical examples so that you learn by doing. AI is code that mimics certain tasks. You can use AI to predict trends like the stock market. Automating tasks has exploded in popularity since TensorFlow became available to the public (like you and me!) AI like TensorFlow is great for automated tasks including facial recognition. One farmer used the machine model to pick cucumbers! Join Mammoth Interactive in this course, where we blend theoretical knowledge with hands-on coding projects to teach you everything you need to know as a beginner to image recognition. Enroll today to join the Mammoth community! | https://www.udemy.com/course/pythondatascience/#instructor-1 | Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvard’s edX, Business Insider and more. Over 11 years, Mammoth Interactive has built a global student community with 1.1 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content. Founder and CEO John Bura has been programming since 1997 and teaching since 2002. John has created top-selling applications for iOS, Xbox and more. John also runs SaaS company Devonian Apps, building efficiency-minded software for technology workers like you. "I absolutely love this course. This is such a comprehensive course that was well worth the money I spent and a lot more. Will definitely be looking at more Mammoth Interactive courses when I finish this." – Student Matt W. "Very good at explaining the basics then building to more complex features." – Student Kevin L. Try a course today. | Image Recognition | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=10 Lakh | |||||||||||||||||
Learn Data structures & Algorithms using Python for Freshers | Learn to master Data structure Algorithm by understanding concepts through time complexity and implementation in Python3 | 2.4 | 195 | 21636 | Created by Chandramouli Jayendran | Apr-21 | English | $9.99 | 19h 22m total length | https://www.udemy.com/course/data-structure-algorithms-for-beginners-for-data-science/ | Chandramouli Jayendran | Office productivity, Data analyst and stock trader | 3.5 | 609 | 58874 | The course Data structures and Algorithm using Python covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. You will learn a lot of theory: how to sort data and how it helps for searching. How to break a large problem into pieces and solve them recursively and it makes sense to proceed greedily. Implemented all the concepts using Python 3 using Pycharm IDE and explained the time complexity and difficulty of the data structures. This course contains of these below mentioned topic: Recursion. Algorithm run time analysis Arrays Stack Linked list Data Structure Binary Tree Binary Search Tree AVL Tree Heap tree Queue Sorting Hash Table Graph Theory Magic Framework Computer Programming Dynamic Programming | https://www.udemy.com/course/data-structure-algorithms-for-beginners-for-data-science/#instructor-1 | I am a software engineer turned into stock trader. Author of 12+ courses with more than 50K students enrolled. I am very passionate on teaching office productivity, software programming and stock market analysis. Worked with teaching several corporate on Office productivity and Programming. Running an teaching centre of my own. Trade in stock market whenever I could see opportunity. | Python | Yes | >=2 | Below 1K | >=20K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Statistics with R - Advanced Level | Advanced statistical analyses using the R program | 4.3 | 194 | 27951 | Created by Bogdan Anastasiei | Dec-20 | English | $9.99 | 4h 15m total length | https://www.udemy.com/course/statistics-with-r-advanced-level/ | Bogdan Anastasiei | University Teacher and Consultant | 4.5 | 7750 | 296607 | If you want to learn how to perform real advanced statistical analyses in the R program, you have come to the right place. Now you don’t have to scour the web endlessly in order to find how to do an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step. So, what’s covered in this course? First of all, we are going to study some more techniques to evaluate the mean differences. If you took the intermediate course- which I highly recommend you – you learned about the t tests and the between-subjects analysis of variance. Now we will go to the next level and tackle the analysis of covariance, the within-subjects analysis of variance and the mixed analysis of variance. Next, in the section about the predictive techniques, we will approach the logistic regression, which is used when the dependent variable is not continuous – in other words, it is categorical. We are going to study three types of logistic regression: binomial, ordinal and multinomial. Then we are going to deal with the grouping techniques. Here you will find out, in detail, how to perform the multidimensional scaling, the principal component analysis and the factor analysis, the simple and the multiple correspondence analysis, the cluster analysis (both k-means and hierarchical) , the simple and the multiple discriminant analysis. So after finishing this course, you will be a real expert in statistical analysis with R – you will know a lot of sophisticated, state-of-the art analysis techniques that will allow you to deeply scrutinize your data and get the most information out of it. So don’t wait, enroll today! | https://www.udemy.com/course/statistics-with-r-advanced-level/#instructor-1 | My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting. | Statistics | Consultant | Yes | >=4 | Below 1K | >=25K | >=4 | Below 10 K | >=2.5 Lakh | ||||||||||||||||
Machine Learning with Python : COMPLETE COURSE FOR BEGINNERS | Complete Machine Learning Course with Python for beginners | 4 | 193 | 23553 | Created by Prashant Mishra | Mar-22 | English | $9.99 | 13h 12m total length | https://www.udemy.com/course/machine-learning-a-z-with-python-with-project-beginner/ | Prashant Mishra | Teacher | 4.2 | 951 | 79722 | Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! Machine Learning (Complete course Overview) Foundations Introduction to Machine Learning Intro Application of machine learning in different fields. Advantage of using Python libraries. (Python for machine learning). Python for AI & ML Python Basics Python functions, packages, and routines. Working with Data structure, arrays, vectors & data frames. (Intro Based with some examples) Jupyter notebook- installation & function Pandas, NumPy, Matplotib, Seaborn Applied Stastistics Descriptive statistics Probability & Conditional Probability Hypothesis Testing Inferential Statistics Probability distributions – Types of distribution – Binomial, Poisson & Normal distribution Machine Learning Supervised Learning Multiple variable Linear regression Regression Introduction to Regression Simple linear regression Model Evaluation in Regression Models Evaluation Metrics in Regression Models Multiple Linear Regression Non-Linear Regression Naïve bayes classifiers Multiple regression K-NN classification Support vector machines Unsupervised Learning Intro to Clustering K-means clustering High-dimensional clustering Hierarchical clustering Dimension Reduction-PCA Classification Introduction to Classification K-Nearest Neighbours Evaluation Metrics in Classification Introduction to decision tress Building Decision Tress Into Logistic regression Logistic regression vs Linear Regression Logistic Regression training Support vector machine Ensemble Techniques Decision Trees Bagging Random Forests Boosting Featurization, Model selection & Tuning Feature engineering Model performance ML pipeline Grid search CV K fold cross-validation Model selection and tuning Regularising Linear models Bootstrap sampling Randomized search CV Recommendation Systems Introduction to recommendation systems Popularity based model Hybrid models Content based recommendation system Collaborative filtering Additional Modules EDA Pandas-profiling library Time series forecasting ARIMA Approach Model Deployment Kubernetes Capstone Project If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! Our Learner's Review: Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh is kind enough to share his practical experiences and actual problems faced by data scientists/ML engineers. The topic of "The ethics of deep learning" is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course. | https://www.udemy.com/course/machine-learning-a-z-with-python-with-project-beginner/#instructor-1 | I am Computer Science Graduate in 2021 and with a passion for teaching, started back as a BDA in various Ed-tech companies, which increased a little more passion towards this industry to explore. Have trained more than 5000+ Individual students one-on-one and group-based, which not only found my classes very interesting but also developed a huge scope of job opportunities in the future. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=20K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Create Interactive Dashboards in Python by Plotly Dash | Create interactive data science web dashboards using the Plotly data visualizations library and Dash library in Python. | 3.9 | 192 | 1703 | Created by Mubeen Ali | Mar-22 | English | $9.99 | 15h 11m total length | https://www.udemy.com/course/create-interactive-dashboards-in-python-by-plotly-dash/ | Mubeen Ali | MSc Data Science | 4 | 372 | 10627 | In this course, you will learn how to create interactive web based dashboards in python using the plotly data visualizations library and dash library. Now it is easy to create data analytics web dashboards using dash library. We can create dashboards like business intelligence and we can upload these dashboards on live servers. Dash apps or dashboards are easily viewable on all devices as well as on mobile devices. I have used many useful dash input and output components to create interactive dashboards. Input components help us to get specific data from the dataset. Using chained callback, we can create dependent input components. You will learn in this course the use of following dash input components. Drop down list (Dependent drop down list) Range slider Slider Radio items Interval You will learn in this course many chart types. Bar chart Line cart Pie chart Donut chart Bubble chart Indicators Scatter map box map chart Dash data table Html table in dash You will learn in this course how to create an interactive layout for the dashboard using CSS style sheets. We can create attractive web dashboards using a dash library with CSS properties. We can create columns and rows in dash app or dashboard as well as in html page and design the dashboard layout as android app. After watching video lessons, you will be able to create your own dashboards using dash library. Note:- I have created extra 34 python dashboards in plotly dash and added in section four as for assignments. You can download these dashboards and re-create these yourselves. This will help you to get deep knowledge about the plotly data visualizations library and dash library. | https://www.udemy.com/course/create-interactive-dashboards-in-python-by-plotly-dash/#instructor-1 | I am passionate about learning Dash. Dash is a python framework that creates beautiful web based data visualization dashboards. We can deploy dash apps on servers and then share them through links. Dash apps are mobile friendly and can be viewed on mobile devices as well as all other devices. | Python | >=3 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Clustering & Classification With Machine Learning In R | Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In R -- With Practical Examples | 4.6 | 191 | 2153 | Created by Minerva Singh | Oct-22 | English | $9.99 | 8h 6m total length | https://www.udemy.com/course/clustering-classification-with-machine-learning-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | HERE IS WHY YOU SHOULD TAKE THIS COURSE: This course your complete guide to both supervised & unsupervised learning using R... That means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised & supervised learning in R, you can give your company a competitive edge and boost your career to the next level. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic... This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. Unlike other R instructors, I dig deep into the machine learning features of R and gives you a one-of-a-kind grounding in Data Science! You will go all the way from carrying out data reading & cleaning to machine learning to finally implementing powerful machine learning algorithms and evaluating their performance using R. THIS COURSE HAS 8 SECTIONS COVERING EVERY ASPECT OF R MACHINE LEARNING: • A full introduction to the R Framework for data science • Data Structures and Reading in R, including CSV, Excel and HTML data • How to Pre-Process and “Clean” data by removing NAs/No data,visualization • Machine Learning, Supervised Learning, Unsupervised Learning in R • Model building and selection...& MUCH MORE! By the end of the course, you’ll have the keys to the entire R Machine Learning Kingdom! NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use data science packages like caret to work with real data in R... You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning. Again, we'll work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW! | https://www.udemy.com/course/clustering-classification-with-machine-learning-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Machine Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Deep Learning: Visual Exploration | Deep neural networks visually explained in plain english & without complex math | 4.6 | 191 | 11539 | Created by Vladimir Grankin | Apr-18 | English | $29.99 | 4h 1m total length | https://www.udemy.com/course/deep-learning-visual-exploration-for-deep-understanding/ | Vladimir Grankin | Software Engineer | 4.3 | 942 | 52351 | Visual introduction to Deep Learning based on simple deep neural network. Take this course if you want to understand the magic behind deep neural networks and to get a excellent visual intuition on what is happening under the hood when data is travelling through the network and ends up as a prediction at it's output. In this course we will fully demystify such concepts as weights, biases and activation functions. You will visually see what exactly they are doing and how neural network uses these components to come up with accurate predictions. | https://www.udemy.com/course/deep-learning-visual-exploration-for-deep-understanding/#instructor-1 | Hi! I'm Vladimir. I have more than 10 000 hours (and 10+ years) in software engineering and I have worked for different IT companies during these years. I also have a Bachelor's degree in Computer Science. I have my own blog, which is dedicated to software development. I'm interested in everything related to IT and I follow IT technologies development with pleasure! I also learn new tools and implement them in my daily work. I strive to share my knowledge with people! | Deep Learning | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Image Recognition with Neural Networks From Scratch | Write An Image Recognition Program in Python | 3.6 | 190 | 9671 | Created by Long Nguyen | Jan-20 | English | $9.99 | 3h 1m total length | https://www.udemy.com/course/image-recognition-with-neural-networks-from-scratch/ | Long Nguyen | Faculty | 4.2 | 327 | 14980 | This is an introduction to Neural Networks. The course explains the math behind Neural Networks in the context of image recognition. By the end of the course, we will have written a program in Python that recognizes images without using any autograd libraries. The only prerequisite is some high school precalculus. Although the prerequisite is minimal, we will discuss many advanced topics including: 1) functions and their computational graphs. 2) neural networks 3) conceptually understand the derivative and the gradient. 4) gradient descent and backpropagation 5) the multivariable chain rule 6) mini-batch gradient descent | https://www.udemy.com/course/image-recognition-with-neural-networks-from-scratch/#instructor-1 | I am currently a faculty at Boston Latin School and a lecturer at the University of Massachusetts Boston(Umass Boston). I received both my Masters and Ph.D. in Mathematics at Brigham Young University. I am passionate about teaching and my interest is to bring interesting ideas in math and computer science accessible to a wide audience. | Neural Networks | >=3 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Machine Learning MASTER, Zero to Mastery | To Being Machine Learning Mystery | 3.7 | 190 | 27619 | Created by Data Science ACADEMY | Jan-22 | English | $9.99 | 63h 26m total length | https://www.udemy.com/course/ml-master/ | Data Science ACADEMY | ML Master Trainer | 3.9 | 256 | 31575 | Machine Learning MASTER To being Machine Learning Mystery I am sure a number of you have heard about machine learning. A dozen of you might even know what it is. And a couple of you might have worked with machine learning algorithms too. You see where this is going? Not a lot of people are familiar with the technology that will be absolutely essential 5 years from now. Siri is machine learning. Amazon’s Alexa is machine learning. Ad and shopping item recommender systems are machine learning. Let’s try to understand machine learning with a simple analogy of a 2 year old boy. Just for fun, let’s call him Kylo Ren Let’s assume Kylo Ren saw an elephant. What will his brain tell him ?(Remember he has minimum thinking capacity, even if he is the successor to Vader). His brain will tell him that he saw a big moving creature which was grey in color. He sees a cat next, and his brain tells him that it is a small moving creature which is golden in color. Finally, he sees a light saber next and his brain tells him that it is a non-living object which he can play with! His brain at this point knows that saber is different from the elephant and the cat, because the saber is something to play with and doesn’t move on its own. His brain can figure this much out even if Kylo doesn’t know what movable means. This simple phenomenon is called Clustering . Machine learning is nothing but the mathematical version of this process. A lot of people who study statistics realized that they can make some equations work in the same way as brain works. Brain can cluster similar objects, brain can learn from mistakes and brain can learn to identify things. All of this can be represented with statistics, and the computer based simulation of this process is called Machine Learning. Why do we need the computer based simulation? because computers can do heavy math faster than human brains. I would love to go into the mathematical/statistical part of machine learning but you don’t wanna jump into that without clearing some concepts first. Let’s get back to Kylo Ren. Let’s say Kylo picks up the saber and starts playing with it. He accidentally hits a stormtrooper and the stormtrooper gets injured. He doesn’t understand what’s going on and continues playing. Next he hits a cat and the cat gets injured. This time Kylo is sure he has done something bad, and tries to be somewhat careful. But given his bad saber skills, he hits the elephant and is absolutely sure that he is in trouble. He becomes extremely careful thereafter, and only hits his dad on purpose as we saw in Force Awakens!! | https://www.udemy.com/course/ml-master/#instructor-1 | Data Science, Machine Learning, Artifical Intelligence, Deep Learning, Search Engine Optimization, Search Engine Marketing, Computatioal Methods and also Python Programming Language Training. Python, Data Science, Machine Learning, Deep Learning and Artificial Intelligence, we combine and present our lessons with real life examples. in order to extend the knowledge you have learned beyond the general level of culture. 10+ Years Experience | Machine Learning | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | >=25K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
A/B Testing in Python | Learn How To Define, Start, And Analyze The Results Of An A/B Test. Improve Business Performance Through A/B Testing | 4.2 | 188 | 1480 | Created by 365 Careers, Anastasia K | Apr-22 | English | $9.99 | 2h 57m total length | https://www.udemy.com/course/ab-testing-in-python/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 614655 | 2122763 | A/B testing is a tool that helps companies make reliable decisions based on data. This is one of the fundamental skills you need to land a job as a data scientist or data analyst. Do you want to become a data scientist or a data analyst? If you do, this is the perfect course for you! Your instructor Anastasia is a senior data scientist working at a Stockholm-based music streaming startup. She has earned two Master's degrees in Business Intelligence and Computer Science, and grown from a recent graduate to a Senior role in just 3 years. Anastasia has performed a significant number of A/B tests for large tech companies with hundreds of millions monthly users. By taking this course, you will learn how to: · Define an A/B test · Start an A/B test · Analyse the results of an A/B test on your own Along your learning journey Anastasia will walk you through an A/B testing process for a fictional company with a digital product. This case study unfolds throughout the course and touches on everything from the very beginning of the A/B testing process to the very end including some advanced considerations. Moreover, Anastasia takes some time to share with you her advice on how to prepare for the questions on the A/B test interview for a data scientist or data analyst position. One strong point of differentiation from statistical textbooks and theoretical trainings is that the A/B Testing in Python course will teach you how to design A/B tests for digital products that have millions or hundreds of millions of users. It is a rare overview of the A/B testing process from a business, technical, and data analysis perspective. This is the perfect course for you if you are: - a data science student who wants to learn one of the fundamental skills needed on the job - junior data scientists with no experience with A/B testing - software developers and product managers who want to learn how to run A/B tests in their company to improve the product they are building You will learn an invaluable skill that can transform a company’s business (and your career along the way). So, what are you waiting for? Click the ‘Buy now’ button and let’s begin this journey today! | https://www.udemy.com/course/ab-testing-in-python/#instructor-1 | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Python | >=4 | Below 1K | Below 10K | >=4 | >=5 Lakh | >=10 Lakh | ||||||||||||||||||
Data Science Mega-Course: #Build {120-Projects In 120-Days} | Build & Deploy Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Azure, GCP, Heruko Cloud) | 4.3 | 188 | 3645 | Created by Pianalytix . | Sep-22 | English | $9.99 | 132h 34m total length | https://www.udemy.com/course/real-world-data-science-projects-using-python/ | Pianalytix . | Technology For Innovators | 4.6 | 1350 | 65362 | In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud). According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary. This makes Data Science a highly lucrative career choice. It is mainly due to the dearth of Data Scientists resulting in a huge income bubble. Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics, and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market. A Data Scientist enjoys a position of prestige in the company. The company relies on its expertise to make data-driven decisions and enable them to navigate in the right direction. Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales. A healthcare company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize. Still, the pay scale of Data scientists is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills. Due to several lucrative perks, Data Science is an attractive field. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career. In This Course, We Are Going To Work On 120 Real World Projects Listed Below: Project-1: Pan Card Tempering Detector App -Deploy On Heroku Project-2: Dog breed prediction Flask App Project-3: Image Watermarking App -Deploy On Heroku Project-4: Traffic sign classification Project-5: Text Extraction From Images Application Project-6: Plant Disease Prediction Streamlit App Project-7: Vehicle Detection And Counting Flask App Project-8: Create A Face Swapping Flask App Project-9: Bird Species Prediction Flask App Project-10: Intel Image Classification Flask App Project-11: Language Translator App Using IBM Cloud Service -Deploy On Heroku Project-12: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku Project-13: Laptop Price Predictor -Deploy On Heroku Project-14: WhatsApp Text Analyzer -Deploy On Heroku Project-15: Course Recommendation System -Deploy On Heroku Project-16: IPL Match Win Predictor -Deploy On Heroku Project-17: Body Fat Estimator App -Deploy On Microsoft Azure Project-18: Campus Placement Predictor App -Deploy On Microsoft Azure Project-19: Car Acceptability Predictor -Deploy On Google Cloud Project-20: Book Genre Classification App -Deploy On Amazon Web Services Project 21 : DNA classification for finding E.Coli - Deploy On AWS Project 22 : Predict the next word in a sentence. - AWS - Deploy On AWS Project 23 : Predict Next Sequence of numbers using LSTM - Deploy On AWS Project 24 : Keyword Extraction from text using NLP - Deploy On Azure Project 25 : Correcting wrong spellings - Deploy On Azure Project 26 : Music popularity classification - Deploy On Google App Engine Project 27 : Advertisement Classification - Deploy On Google App Engine Project 28 : Image Digit Classification - Deploy On AWS Project 29 : Emotion Recognition using Neural Network - Deploy On AWS Project 30 : Breast cancer Classification - Deploy On AWS Project-31: Sentiment Analysis Django App -Deploy On Heroku Project-32: Attrition Rate Django Application Project-33: Find Legendary Pokemon Django App -Deploy On Heroku Project-34: Face Detection Streamlit App Project-35: Cats Vs Dogs Classification Flask App Project-36: Customer Revenue Prediction App -Deploy On Heroku Project-37: Gender From Voice Prediction App -Deploy On Heroku Project-38: Restaurant Recommendation System Project-39: Happiness Ranking Django App -Deploy On Heroku Project-40: Forest Fire Prediction Django App -Deploy On Heroku Project-41: Build Car Prices Prediction App -Deploy On Heroku Project-42: Build Affair Count Django App -Deploy On Heroku Project-43: Build Shrooming Predictions App -Deploy On Heroku Project-44: Google Play App Rating prediction With Deployment On Heroku Project-45: Build Bank Customers Predictions Django App -Deploy On Heroku Project-46: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku Project-47: Build Medical Cost Predictions Django App -Deploy On Heroku Project-48: Phishing Webpages Classification Django App -Deploy On Heroku Project-49: Clothing Fit-Size predictions Django App -Deploy On Heroku Project-50: Build Similarity In-Text Django App -Deploy On Heroku Project-51: Black Friday Sale Project Project-52: Sentiment Analysis Project Project-53: Parkinson’s Disease Prediction Project Project-54: Fake News Classifier Project Project-55: Toxic Comment Classifier Project Project-56: IMDB Movie Ratings Prediction Project-57: Indian Air Quality Prediction Project-58: Covid-19 Case Analysis Project-59: Customer Churning Prediction Project-60: Create A ChatBot Project-61: Video Game sales Analysis Project-62: Zomato Restaurant Analysis Project-63: Walmart Sales Forecasting Project-64 : Sonic wave velocity prediction using Signal Processing Techniques Project-65 : Estimation of Pore Pressure using Machine Learning Project-66 : Audio processing using ML Project-67 : Text characterisation using Speech recognition Project-68 : Audio classification using Neural networks Project-69 : Developing a voice assistant Project-70 : Customer segmentation Project-71 : FIFA 2019 Analysis Project-72 : Sentiment analysis of web scrapped data Project-73 : Determining Red Vine Quality Project-74 : Customer Personality Analysis Project-75 : Literacy Analysis in India Project-76: Heart Attack Risk Prediction Using Eval ML (Auto ML) Project-77: Credit Card Fraud Detection Using Pycaret (Auto ML) Project-78: Flight Fare Prediction Using Auto SK Learn (Auto ML) Project-79: Petrol Price Forecasting Using Auto Keras Project-80: Bank Customer Churn Prediction Using H2O Auto ML Project-81: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML) Project-82: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML) Project-83: Pizza Price Prediction Using ML And EVALML(Auto ML) Project-84: IPL Cricket Score Prediction Using TPOT (Auto ML) Project-85: Predicting Bike Rentals Count Using ML And H2O Auto ML Project-86: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML) Project-87: Bangalore House Price Prediction Using Auto SK Learn (Auto ML) Project-88: Hospital Mortality Prediction Using PyCaret (Auto ML) Project-89: Employee Evaluation For Promotion Using ML And Eval Auto ML Project-90: Drinking Water Potability Prediction Using ML And H2O Auto ML Project-91: Image Editor Application With OpenCV And Tkinter Project-92: Brand Identification Game With Tkinter And Sqlite3 Project-93: Transaction Application With Tkinter And Sqlite3 Project-94: Learning Management System With Django Project-95: Create A News Portal With Django Project-96: Create A Student Portal With Django Project-97: Productivity Tracker With Django And Plotly Project-98: Create A Study Group With Django Project-99: Building Crop Guide Application with PyQt5, SQLite Project-100: Building Password Manager Application With PyQt5, SQLite Project-101: Create A News Application With Python Project-102: Create A Guide Application With Python Project-103: Building The Chef Web Application with Django, Python Project-104: Syllogism-Rules of Inference Solver Web Application Project-105: Building Vision Web Application with Django, Python Project-106: Building Budget Planner Application With Python Project-107: Build Tic Tac Toe Game Project-108: Random Password Generator Website using Django Project-109: Building Personal Portfolio Website Using Django Project-110: Todo List Website For Multiple Users Project-111: Crypto Coin Planner GUI Application Project-112: Your Own Twitter Bot -python, request, API, deployment, tweepy Project-113: Create A Python Dictionary Using python, Tkinter, JSON Project-114: Egg-Catcher Game using python Project-115: Personal Routine Tracker Application using python Project-116: Building Screen -Pet using Tkinter & Canvas Project-117: Building Caterpillar Game Using Turtle and Python Project-118: Building Hangman Game Using Python Project-119: Developing our own Smart Calculator Using Python and Tkinter Project-120: Image-based steganography Using Python and pillows Tip: Create A 60 Days Study Plan Or 120 Day Study Plan, Spend 1-3hrs Per Day, Build 120 Projects In 60 Days Or 120 Projects In 120 Days. The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires. | https://www.udemy.com/course/real-world-data-science-projects-using-python/#instructor-1 | Pianalytix Edutech Pvt Ltd uses cutting-edge AI technology & innovative product design to help users learn Machine Learning more efficiently and to implement Machine Learning in the real world. Pianalytix also leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability. | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Natural Language Processing for Text Summarization | Understand the basic theory and implement three algorithms step by step in Python! Implementations from scratch! | 4.3 | 188 | 14733 | Created by Jones Granatyr, IA Expert Academy | Feb-22 | English | $9.99 | 4h 55m total length | https://www.udemy.com/course/text-summarization-natural-language-processing-python/ | Jones Granatyr | Professor | 4.7 | 34416 | 158021 | The area of Natural Language Processing (NLP) is a subarea of Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q&A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Another important application is the automatic document summarization, which consists of generating text summaries. Suppose you need to read an article with 50 pages, however, you do not have enough time to read the full text. In that case, you can use a summary algorithm to generate a summary of this article. The size of this summary can be adjusted: you can transform 50 pages into only 20 pages that contain only the most important parts of the text! Based on this, this course presents the theory and mainly the practical implementation of three text summarization algorithms: (i) frequency-based, (ii) distance-based (cosine similarity with Pagerank) and (iii) the famous and classic Luhn algorithm, which was one of the first efforts in this area. During the lectures, we will implement each of these algorithms step by step using modern technologies, such as the Python programming language, the NLTK (Natural Language Toolkit) and spaCy libraries and Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine. In addition to implementing the algorithms, you will also learn how to extract news from blogs and the feeds, as well as generate interesting views of the summaries using HTML! After implementing the algorithms from scratch, you have an additional module in which you can use specific libraries to summarize documents, such as: sumy, pysummarization and BERT summarizer. At the end of the course, you will know everything you need to create your own summary algorithms! If you have never heard about text summarization, this course is for you! On the other hand, if you are already experienced, you can use this course to review the concepts. | https://www.udemy.com/course/text-summarization-natural-language-processing-python/#instructor-1 | Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios. | NLP | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | >=1.5 Lakh | |||||||||||||||||
Artificial Intelligence for Simple Games | Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games! | 4.8 | 188 | 2034 | Created by Jan Warchocki, Ligency I Team, Ligency Team | Feb-21 | English | $11.99 | 12h 23m total length | https://www.udemy.com/course/artificial-intelligence-for-simple-games/ | Jan Warchocki | Artificial Intelligence Engineer | 4.5 | 188 | 905564 | Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming? If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge. Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more. 1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond. 2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks. 3. Dive into SuperDataScience’s much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies. 4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry. ‘AI for Simple Games’ Curriculum Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible! Section #2 — Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out. Section #3 — Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games! Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake. | https://www.udemy.com/course/artificial-intelligence-for-simple-games/#instructor-1 | Hi! My name is Jan Warchocki and I'm an Artificial Intelligence specialist from Poland. Around two years ago I have started my AI journey with SuperDataScience and Hadelin de Ponteves' courses on Udemy. Since then I have created multiple AI projects on my own, which are based on subjects such as Reinforcement Learning and Deep Learning. By building those models I have broadened my experience and knowledge on these topics. Today I would like to share with you the knowledge that I have gained throughout my journey. I sincerely hope that I will encourage you to start your own AI journey! | Artificial Intelligence | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | >=5 Lakh | |||||||||||||||||
Python Pandas Library Full Tutorial | Pandas Library | 4.7 | 188 | 19243 | Created by Diptam Paul | Apr-20 | English | $9.99 | 1h 14m total length | https://www.udemy.com/course/python-pandas-library/ | Diptam Paul | Computer Science Engineer | 4.2 | 1138 | 76758 | pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. In this course, you'll learn a lot about this library. Basic knowledge of Numpy is required, as we will perform some tasks using NumPy. [Note: In this course, we used Jupyter Notebook to write all the codings. in case if you don't have Jupyter Notebook or you don't how to use Jupyter Notebook, you can simply run these codes in any IDE, or even in Python Default IDLE.] | https://www.udemy.com/course/python-pandas-library/#instructor-1 | I'm a computer science student, love to teach what I know. Sharing knowledge is the best thing in this world. I've knowledge of Web Development and Designing, and on machine learning, Data Science and AI. I'll make courses on these topics so that everyone can understand these topics in an easy way. | Python | Engineer/Developer | >=4 | Below 1K | >=15K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
TensorFlow 101: Introduction to Deep Learning | Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning. | 4.5 | 188 | 5174 | Created by Sefik Ilkin Serengil | May-20 | English | $9.99 | 3h 55m total length | https://www.udemy.com/course/tensorflow-101-introduction-to-deep-learning/ | Sefik Ilkin Serengil | Software Engineer | 4.1 | 577 | 6911 | This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace). This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before. | https://www.udemy.com/course/tensorflow-101-introduction-to-deep-learning/#instructor-1 | Serengil received his MSc in Computer Science from Galatasaray University in 2011. He has been working as a software developer for a fintech company since 2010. Currently, he is a member of AI and Machine Learning team as a Data Scientist in this company. His current research interests are Machine Learning, particularly applications of Deep Learning and Cryptography in particular Elliptic Curve cryptosystems. Serengil contributed many open source projects as well. Repositories he pushed to GitHub got hundreds of stars and forks, and thousands of installations as well. | Deep Learning | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Natural Language Processing: NLP In Python with 2 Projects | Learn NLP with Machine Learning Algorithms, Spacy, NLTK, TextBlob for Text Processing, Text Classification and Much More | 4.5 | 186 | 19822 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 2h 59m total length | https://www.udemy.com/course/nlp-bootcamp-machine-learning-deep-learning/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Interested in Learning Natural Language Processing? This course is a perfect fit for you. This course will take you to step by step into the world of Natural Language Processing. NLP is a subfield of linguistic, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. It will cover all common and important algorithms and will give you the experience of working on some real-world projects. This course will cover the following topics:- 1. Introduction to NLP. 2. Feature Engineering for NLP. 3. Data Cleaning for NLP. 4. Feature Extraction for NLP. 5. Data Visualization for NLP. 6. Text Classification. 7. Cleaning and Pre-processing We have covered each and every topic in detail and also learned to apply them to real-world problems. You don't know anything about NLP? calm down !!!.. I am always available to answer your questions and help you along your data science journey. See you in class! There are lots and lots of exercises for you to practice and also 2 bonus NLP Projects "Sentiment analyzer" and "Drugs Prescription using Reviews". In this Sentiment analyzer project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights. In this Drugs Prescription using Reviews project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights. This is a great test for people who are learning the Python language and data science and are looking for new challenges. You will make use of all the topics read in this course. I am expecting you to know basic knowledge of python and your curiosity to learn Different techniques in NLP world. After finishing the course you should able to build your own basic NLP applications You will also have access to all the resources used in this course. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! Enroll now and become a master in machine learning. | https://www.udemy.com/course/nlp-bootcamp-machine-learning-deep-learning/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | NLP | Grandmaster | >=4 | Below 1K | >=15K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Machine Learning with Python Comprehensive Course 2022 | Learn core concepts of Machine Learning. Apply ML techniques to real-world problems and develop AI/ML based applications | 4.3 | 185 | 39138 | Created by Uplatz Training | Jan-21 | English | $9.99 | 63h 24m total length | https://www.udemy.com/course/machine-learning-concepts-and-application-of-ml-using-python/ | Uplatz Training | Fastest growing Global IT Training Provider | 3.7 | 12189 | 381859 | A warm welcome to the Machine Learning with Python course by Uplatz. The Machine Learning with Python course aims to teach students/course participants some of the core ideas in machine learning, data science, and AI that will help them go from a real-world business problem to a first-cut, working, and deployable AI solution to the problem. Our main goal is to enable participants use the skills they acquire in this course to create real-world AI solutions. We'll aim to strike a balance between theory and practice, with a focus on the practical and applied elements of ML. This Python-based Machine Learning training course is designed to help you grasp the fundamentals of machine learning. It will provide you a thorough knowledge of Machine Learning and how it works. As a Data Scientist or Machine Learning engineer, you'll learn about the relevance of Machine Learning and how to use it in the Python programming language. Machine Learning Algorithms will allow you to automate real-life events. We will explore different practical Machine Learning use cases and practical scenarios at the end of this Machine Learning online course and will build some of them. In this Machine Learning course, you'll master the fundamentals of machine learning using Python, a popular programming language. Learn about data exploration and machine learning techniques such as supervised and unsupervised learning, regression, and classifications, among others. Experiment with Python and built-in tools like Pandas, Matplotlib, and Scikit-Learn to explore and visualize data. Regression, classification, clustering, and sci-kit learn are all sought-after machine learning abilities to add to your skills and CV. To demonstrate your competence, add fresh projects to your portfolio and obtain a certificate in machine learning. Machine Learning Certification training in Python will teach you about regression, clustering, decision trees, random forests, Nave Bayes, and Q-Learning, among other machine learning methods. This Machine Learning course will also teach you about statistics, time series, and the many types of machine learning algorithms, such as supervised, unsupervised, and reinforcement algorithms. You'll be solving real-life case studies in media, healthcare, social media, aviation, and human resources throughout the Python Machine Learning Training. Course Outcomes: After completion of this course, student will be able to: Understand about the roles & responsibilities that a Machine Learning Engineer plays Python may be used to automate data analysis Explain what machine learning is Work with data that is updated in real time Learn about predictive modelling tools and methodologies Discuss machine learning algorithms and how to put them into practice Validate the algorithms of machine learning Explain what a time series is and how it is linked to other ideas Learn how to conduct business in the future while living in the now Apply machine learning techniques on real world problem or to develop AI based application Analyze and Implement Regression techniques Solve and Implement solution of Classification problem Understand and implement Unsupervised learning algorithms Objective: Learning basic concepts of various machine learning methods is primary objective of this course. This course specifically make student able to learn mathematical concepts, and algorithms used in machine learning techniques for solving real world problems and developing new applications based on machine learning. Topics Python for Machine Learning Introduction of Python for ML, Python modules for ML, Dataset, Apply Algorithms on datasets, Result Analysis from dataset, Future Scope of ML. Introduction to Machine Learning What is Machine Learning, Basic Terminologies of Machine Learning, Applications of ML, different Machine learning techniques, Difference between Data Mining and Predictive Analysis, Tools and Techniques of Machine Learning. Types of Machine Learning Supervised Learning, Unsupervised Learning, Reinforcement Learning. Machine Learning Lifecycle. Supervised Learning : Classification and Regression Classification: K-Nearest Neighbor, Decision Trees, Regression: Model Representation, Linear Regression. Unsupervised and Reinforcement Learning Clustering: K-Means Clustering, Hierarchical clustering, Density-Based Clustering. Machine Learning - Course Syllabus 1. Linear Algebra Basics of Linear Algebra Applying Linear Algebra to solve problems 2. Python Programming Introduction to Python Python data types Python operators Advanced data types Writing simple Python program Python conditional statements Python looping statements Break and Continue keywords in Python Functions in Python Function arguments and Function required arguments Default arguments Variable arguments Build-in functions Scope of variables Python Math module Python Matplotlib module Building basic GUI application NumPy basics File system File system with statement File system with read and write Random module basics Pandas basics Matplotlib basics Building Age Calculator app 3. Machine Learning Basics Get introduced to Machine Learning basics Machine Learning basics in detail 4. Types of Machine Learning Get introduced to Machine Learning types Types of Machine Learning in detail 5. Multiple Regression 6. KNN Algorithm KNN intro KNN algorithm Introduction to Confusion Matrix Splitting dataset using TRAINTESTSPLIT 7. Decision Trees Introduction to Decision Tree Decision Tree algorithms 8. Unsupervised Learning Introduction to Unsupervised Learning Unsupervised Learning algorithms Applying Unsupervised Learning 9. AHC Algorithm 10. K-means Clustering Introduction to K-means clustering K-means clustering algorithms in detail 11. DBSCAN Introduction to DBSCAN algorithm Understand DBSCAN algorithm in detail DBSCAN program | https://www.udemy.com/course/machine-learning-concepts-and-application-of-ml-using-python/#instructor-1 | Uplatz is UK-based leading IT Training provider serving students across the globe. Our uniqueness comes from the fact that we provide online training courses at a fraction of the average cost of these courses in the market. Over a short span of 3 years, Uplatz has grown massively to become a truly global IT training provider with a wide range of career-oriented courses on cutting-edge technologies and software programming. Our specialization includes Data Science, Data Engineering, SAP, Oracle, Salesforce, AWS, Microsoft Azure, Google Cloud, IBM Cloud, SAS, Python, R, JavaScript, Java, Full Stack Web Development, Mobile App Development, BI & Visualization, Tableau, Power BI, Spotfire, Data warehousing, ETL tools, Informatica, IBM Data Stage, Digital Marketing, Agile, DevOps, and more. Founded in March 2017, Uplatz has seen phenomenal rise in the training industry starting with an online course on SAP FICO and now providing training on 5000+ courses across 103 countries having served 300,000 students in a period of just 3 years. Uplatz's training courses are highly structured, subject-focused, and job-oriented with strong emphasis on practice and assignments. Our courses are designed and taught by more than a thousand highly skilled and experienced tutors who have strong expertise in their areas whether it be AWS, Azure, Adobe, SAP, Oracle, or any other technology or in-demand software. | Machine Learning | >=4 | Below 1K | >=35K | >=3 | Below 1 Lakh | >=3.5 Lakh | ||||||||||||||||||
Microsoft Azure Cognitive Services Crash Course | Build Smart Applications in Minutes with Azure Cognitive Vision, Language, Speech, Decision and Search services | 4.5 | 184 | 7704 | Created by Reza Salehi | Jan-22 | English | $9.99 | 4h 50m total length | https://www.udemy.com/course/azure-cognitive-services-crash-course/ | Reza Salehi | Cloud Consultant and Trainer | 4.5 | 184 | 7704 | This course is a one-stop shop to gain a solid understanding of Azure Cognitive Services. Know all services under Azure Cognitive Services. You will list them all. Decide if any of these APIs can help with your business scenario. If not, know where to look next. Understand what each of these APIs do. Microsoft documentation is referenced for further research. Gain hands-on experience following the demos. Taking this course will help with the Microsoft Exam AI-100: Designing and Implementing an Azure AI Solution. | https://www.udemy.com/course/azure-cognitive-services-crash-course/#instructor-1 | Reza started his professional IT career in 2000 with web-based game development. Since then, he has mostly focused on designing and developing IT solutions using Microsoft technologies such as ASP.NET, Web API, .NET Core, SQL Server and the Azure cloud. Reza is passionate about cloud computing and has delivered several in-class, remote and on-demand courses. He continues to help his clients moving to Azure and AWS while sharing his expertise with fellow developers and engineers through training. He earned his Microsoft Certified Trainer (MCT) status in 2008. | Azure | Consultant | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Become an AWS SageMaker Machine Learning Engineer in 30 Days | [2022] Build 30+ ML Projects in 30 Days in AWS, Master SageMaker JumpStart, Canvas, AutoPilot, DataWrangler, Lambda & S3 | Bestseller | 4.6 | 184 | 2039 | Created by Dr. Ryan Ahmed, Ph.D., MBA | Jun-22 | English | $9.99 | 41h 17m total length | https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | Do you want to become an AWS Machine Learning Engineer Using SageMaker in 30 days? Do you want to build super powerful production-level Machine Learning (ML) applications in AWS but don’t know where to start? Are you an absolute beginner and want to break into AI, ML and Cloud Computing and looking for a course that includes everything you need? Are you an aspiring entrepreneur who wants to maximize business revenues and reduce costs with ML but don’t know how to get there quickly and efficiently? If the answer is yes to any of these questions, then this course is for you! Machine Learning is the future one of the top tech fields to be in right now! ML and AI will change our lives in the same way electricity did 100 years ago. ML is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospects. AWS is the one of the most widely used cloud computing platforms in the world and several companies depend on AWS for their cloud computing purposes. AWS SageMaker is a fully managed service offered by AWS that allows data scientist and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently. This course is unique and exceptional in many ways, it includes several practice opportunities, quizzes, and final capstone projects. In this course, students will learn how to create production-level ML models using AWS. The course is divided into 8 main sections as follows: Section 1 (Days 1 – 3): we will learn the following: (1) Start with an AWS and Machine Learning essentials “starter pack” that includes key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch, (2) The benefits of cloud computing, the difference between regions and availability zones and what’s included in the AWS Free Tier Package, (3) How to setup a brand-new account in AWS, setup a Multi-Factor Authentication (MFA) and navigate through the AWS Management Console, (4) How to monitor billing dashboard, set alarms, S3/EC2 instances pricing and request service limits increase, (5) The fundamentals of Machine Learning and understand the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL), (6) Learn the difference between supervised, unsupervised and reinforcement learning, (7) List the key components to build any machine learning models including data, model, and compute, (8) Learn the fundamentals of Amazon SageMaker, SageMaker Components, training options offered by SageMaker including built-in algorithms, AWS Marketplace, and customized ML algorithms, (9) Cover AWS SageMaker Studio and learn the difference between AWS SageMaker JumpStart, SageMaker Autopilot and SageMaker Data Wrangler, (10) Learn how to write our first code in the cloud using Jupyter Notebooks. We will then have a tutorial covering AWS Marketplace object detection algorithms such as Yolo V3, (11) Learn how to train our first machine learning model using the brand-new AWS SageMaker Canvas without writing any code! Section 2 (Days 4 – 5): we will learn the following: (1) Label images and text using Amazon SageMaker GroundTruth, (2) learn the difference between data labeling workforces such as public mechanical Turks, private labellers and AWS curated third-party vendors, (3) cover several companies’ success stories that have leveraged data to maximize revenues, reduce costs and optimize processes, (4) cover data sources, types, and the difference between good and bad data, (5) learn about Json Lines formats and Manifest Files, (6) cover a detailed tutorial to define an image classification labeling job in SageMaker, (7) auto-labeling workflow and learn the difference between SageMaker GroundTruth and GroundTruth Plus, (8) learn how to define a labeling job with bounding boxes (object detection and pixel-level Semantic Segmentation), (9) Label Text data using Amazon SageMaker GroundTruth. Section 3 (Days 6 – 10): we will learn: (1) how to perform exploratory data analysis (EDA), (2) master Pandas, a super powerful open-source library to perform data analysis in Python, (3) analyze corporate employee information using Pandas in Jupyter Notebooks in AWS SageMaker Studio, (4) define a Pandas Dataframe, read CSV data using Pandas, perform basic statistical analysis on the data, set/reset Pandas DataFrame index, select specific columns from the DataFrame, add/delete columns from the DataFrame, Perform Label/integer-based elements selection, perform broadcasting operations, and perform Pandas DataFrame sorting/ordering, (5) perform statistical data analysis on real world datasets, deal with missing data using pandas, change pandas DataFrame datatypes, define a function, and apply it to a Pandas DataFrame column, perform Pandas operations, and filtering, calculate and display correlation matrix, use seaborn library to show heatmap, (6) analyze cryptocurrency prices and daily returns of Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Cardano (ADA) and Ripple (XRP) using Matplotlib and Seaborn libraries in AWS SageMaker Studio, (7) perform data visualization using Seaborn and Matplotlib libraries, plots include line plot, pie charts, multiple subplots, pairplot, count plot, correlations heatmaps, distribution plot (distplot), Histograms, and Scatterplots, (8) Use Amazon SageMaker Data wrangler in AWS to prepare, clean and visualize the data, (9) understand feature engineering strategies and tools, understand the fundamentals of Data Wrangler in AWS, perform one hot encoding and normalization, perform data visualization Using Data Wrangler, export a data wrangler workflow into Python script, create a custom formula and apply it to a given column in the data, generate summary table tables in Data Wrangler, and generate bias reports. Section 4 (Days 11 – 18): we will learn: (1) machine learning regression fundamentals including simple/multiple linear regression and least sum of squares, (2) build our first simple linear regression model in Scikit-Learn, (3) list all available built-in algorithms in SageMaker, (4) build, train, test and deploy a machine learning regression model using SageMaker Linear Learner algorithm, (5) list machine learning regression algorithms KPIs such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Percentage Error (MPE), Coefficient of Determination (R2), and adjusted R2, (6) Launch a training job using the AWS Management Console and deploy an endpoint without writing any code, (7) cover the theory and intuition behind XG-Boost algorithm and how to use it to solve regression type problems in Scikit-Learn and using SageMaker Built-in algorithms, (8) learn how to train an XG-boost algorithm in SageMaker using AWS JumpStart, assess trained regression models performance, plot the residuals, and deploy an endpoint and perform inference. Section 5 (Days 19 – 20): we will learn: (1) hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization, (2) Understand bias variance trade-off and L1 and L2 regularization, (3) perform hyperparameters optimization using Scikit-Learn library and using SageMaker SDK. Section 6 (Days 21 – 24): we will learn: (1) how to train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier, (2) list the difference between various classifier models KPIs such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), (3) train an XG-boost and Linear Learner algorithms in SageMaker to solve classification type problems, (4) learn the theory and intuition behind K Nearest Neighbors (KNN) in SageMaker and learn how to build, train and test a KNN classifier model in SageMaker. Section 7 (Days 25 – 28): we will learn: (1) how to use AutoGluon library to perform prototyping of AI/ML models using few lines of code, (2) leverage AutoGluon to train multiple regression and classification models and deploy the best one, (3) leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing any code. Section 8 (Days 29 – 30): we will learn: (1) how to define and invoke lambda functions in AWS, (2) understand Machine Learning workflow automation using AWS Lambda, Step functions and SageMaker Pipelines, (3) learn how to define a lambda function in AWS management console, (4) understand the anatomy of Lambda functions, (5) learn how to configure a test event in Lambda, and monitor Lambda invocations in CloudWatch, (6) define a Lambda function using Boto3 SDK, (7) test the lambda function using Eventbridge (cloudwatch events), (8) understand the difference between synchronous and asynchronous invocations, and Invoke a Lambda function using Boto3 SDK. | https://www.udemy.com/course/become-an-aws-machine-learning-engineer-in-30-days-new-2022/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | ||||||||||||||||
Decision Trees, Random Forests, Bagging & XGBoost: R Studio | Decision Trees and Ensembling techinques in R studio. Bagging, Random Forest, GBM, AdaBoost & XGBoost in R programming | 4.4 | 184 | 62690 | Created by Start-Tech Academy | Nov-22 | English | $9.99 | 5h 53m total length | https://www.udemy.com/course/machine-learning-advanced-decision-trees-in-r/ | Start-Tech Academy | 3,000,000+ Enrollments | 4+ Rated | 160+ Countries | 4.5 | 73154 | 1545306 | You're looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in R, right? You've found the right Decision Trees and tree based advanced techniques course! After completing this course you will be able to: Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning. Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in R and analyze its result. Confidently practice, discuss and understand Machine Learning concepts How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course. If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Decision tree, Random Forest, Bagging, AdaBoost and XGBoost. Why should you choose this course? This course covers all the steps that one should take while solving a business problem through Decision tree. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business. What makes us qualified to teach you? The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones: This is very good, i love the fact the all explanation given can be understood by a layman - Joshua Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy Our Promise Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete Assignments With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a decision tree based model, which are some of the most popular Machine Learning model, to solve business problems. Below are the course contents of this course : Section 1 - Introduction to Machine Learning In this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model. Section 2 - R basic This section will help you set up the R and R studio on your system and it'll teach you how to perform some basic operations in R. Section 3 - Pre-processing and Simple Decision trees In this section you will learn what actions you need to take to prepare it for the analysis, these steps are very important for creating a meaningful. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. In the end we will create and plot a simple Regression decision tree. Section 4 - Simple Classification Tree This section we will expand our knowledge of regression Decision tree to classification trees, we will also learn how to create a classification tree in Python Section 5, 6 and 7 - Ensemble technique In this section we will start our discussion about advanced ensemble techniques for Decision trees. Ensembles techniques are used to improve the stability and accuracy of machine learning algorithms. In this course we will discuss Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost. By the end of this course, your confidence in creating a Decision tree model in R will soar. You'll have a thorough understanding of how to use Decision tree modelling to create predictive models and solve business problems. Go ahead and click the enroll button, and I'll see you in lesson 1! Cheers Start-Tech Academy ------------ Below is a list of popular FAQs of students who want to start their Machine learning journey- What is Machine Learning? Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. What are the steps I should follow to be able to build a Machine Learning model? You can divide your learning process into 3 parts: Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part. Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python Understanding of models - Fifth and sixth section cover Classification models and with each theory lecture comes a corresponding practical lecture where we actually run each query with you. Why use R for Machine Learning? Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Machine learning in R 1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing. 2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind. 3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science. 4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R. 5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science. What is the difference between Data Mining, Machine Learning, and Deep Learning? Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions. Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. | https://www.udemy.com/course/machine-learning-advanced-decision-trees-in-r/#instructor-1 | Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners. Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey. Founded by Abhishek Bansal and Pukhraj Parikh. Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python. Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence. | Misc | >=4 | Below 1K | >=50K | >=4 | Below 1 Lakh | >=10 Lakh | ||||||||||||||||||
Essential Fundamentals of R | Data Types and Structures in R , Inputting & Outputting Data, Writing User-Defined Functions, and Manipulating Data Sets | 4 | 183 | 1783 | Created by Geoffrey Hubona, Ph.D. | Jul-20 | English | $9.99 | 10h 32m total length | https://www.udemy.com/course/essential-fundamentals-of-r/ | Geoffrey Hubona, Ph.D. | Associate Professor of Information Systems | 4.1 | 4141 | 31560 | Essential Fundamentals of R is an integrated program that draws from a variety of introductory topics and courses to provide participants with a solid base of knowledge with which to use R software for any intended purpose. No statistical knowledge, programming knowledge, or experience with R software is necessary. Essential Fundamentals of R (7 sessions) covers those important introductory topics basic to using R functions and data objects for any purpose: installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the 'apply' family of functions in R; data set manipulation: and subsetting, and row and column selection. Most sessions present "hands-on" material that demonstrate the execution of R 'scripts' (sets of commands) and utilize many extended examples of R functions, applications, and packages for a variety of common purposes. RStudio, a popular, open source Integrated Development Environment (IDE) for developing and using R applications, is also utilized in the program, supplemented with R-based direct scripts (e.g. 'command-line prompts') when necessary. | https://www.udemy.com/course/essential-fundamentals-of-r/#instructor-1 | Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate). In previous academic faculty positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL; an MA in Economics, also from USF; an MBA in Finance from George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Artificial Intelligence for beginners: Neural Networks | Neural Networks: Learn the basics of artificial intelligence & master the core concepts. Learn artificial intelligence | 3.2 | 182 | 11546 | Created by Hayley - Creative Mind Ch | May-19 | English | $9.99 | 1h 13m total length | https://www.udemy.com/course/neural-networks-concepts/ | Hayley - Creative Mind Ch | Instructor | 4 | 899 | 43070 | Artificial Intelligence is becoming progressively more relevant in today's world. The rise of Artificial intelligence has the potential to transform our future more than any other technology. By using the power of algorithms, you can develop applications which intelligently interact with the world around you, from building intelligent recommender systems to creating self-driving cars, robots and chatbots. Neural networks are a key element of artificial intelligence. Neural networks are one of the most fascinating machine learning models and are used to solve wide range of problems in different areas of artificial intelligence and machine learning. Yet too few really understand how neural networks actually work. This course will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. The purpose of this course is to make neural networks accessible to as many students as possible. In this course I’m going to explain the key aspects of neural networks and provide you with a foundation to get started with advanced topics. You will build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. You’ll understand how to solve complex computational problems efficiently. By the end of this course you will have a fair understanding of how you can leverage the power of artificial intelligence and how to implement neural network models in your applications. Each concept is backed by a generic and real-world problem, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach. Enroll now and start learning artificial intelligence. | https://www.udemy.com/course/neural-networks-concepts/#instructor-1 | Hi, I'm Hayley and I am a coach for more than 5 years and I have applied techniques to help encourage, stretch and clarify my clients’ thinking. So, over the last several years, my mission was to help people overcome countless barriers and help them obtain employment in an area that allows them to flourish. My biggest focus is creating high quality training. I would love to have you as my student and don't hesitate to ask me all your questions in the QA section. | Artificial Intelligence | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | >=10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Data Visualization in Python for Machine Learning Engineers | The Third Course in a Series for Mastering Python for Machine Learning Engineers | 4.6 | 181 | 10254 | Created by Mike West | Jul-21 | English | $10.99 | 1h 7m total length | https://www.udemy.com/course/data-visualization-in-python-for-machine-learning-engineers/ | Mike West | Creator of LogikBot | 4.3 | 19947 | 236930 | Welcome to Data Visualization in Python for Machine learning engineers. This is the third course in a series designed to prepare you for becoming a machine learning engineer. I'll keep this updated and list only the courses that are live. Here is a list of the courses that can be taken right now. Please take them in order. The knowledge builds from course to course. The Complete Python Course for Machine Learning Engineers Data Wrangling in Pandas for Machine Learning Engineers Data Visualization in Python for Machine Learning Engineers (This one) The second course in the series is about Data Wrangling. Please take the courses in order. The knowledge builds from course to course in a serial nature. Without the first course many students might struggle with this one. Thank you!! In this course we are going to focus on data visualization and in Python that means we are going to be learning matplotlib and seaborn. Matplotlib is a Python package for 2D plotting that generates production-quality graphs. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. Seaborn is a Python visualization library based on matplotlib. Most developers will use seaborn if the same functionally exists in both matplotlib and seaborn. This course focuses on visualizing. Here are a few things you'll learn in the course. A complete understanding of data visualization vernacular. Matplotlib from A-Z. The ability to craft usable charts and graphs for all your machine learning needs. Lab integrated. Please don't just watch. Learning is an interactive event. Go over every lab in detail. Real world Interviews Questions. **Five Reasons to Take this Course** 1) You Want to be a Machine Learning Engineer It's one of the most sought after careers in the world. The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. Without a solid understanding of data wrangling in Python you'll have a hard time of securing a position as a machine learning engineer. 2) Data Visualization is a Core Component of Machine Learning Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments. 3) The Growth of Data is Insane Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month. Almost all real world machine learning is supervised. That means you point your machine learning models at clean tabular data. 4) Machine Learning in Plain English Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer. Google expects data engineers and their machine learning engineers to be able to build machine learning models. 5) You want to be ahead of the Curve The data engineer and machine learning engineer roles are fairly new. While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package. Thanks for interest in Data Visualization in Python for Machine learning engineers. See you in the course!! | https://www.udemy.com/course/data-visualization-in-python-for-machine-learning-engineers/#instructor-1 | I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help. I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space. Experience, education and passion I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car. Certifications Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT). Personal Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children. | Machine Learning | >=4 | Below 1K | >=10K | >=4 | Below 1 Lakh | >=2 Lakh | ||||||||||||||||||
Build Neural Networks In Seconds Using Deep Learning Studio | Develop Keras / TensorFlow Deep Learning Models Using A GUI And Without Knowing Python Or Machine Learning | 3.1 | 181 | 565 | Created by Michael Kroeker | Jan-19 | English | $9.99 | 2h 48m total length | https://www.udemy.com/course/build-neural-networks-in-seconds-using-deep-learning-studio/ | Michael Kroeker | Technologist and Data Scientist | 3.4 | 222 | 702 | In this course you will Machine Learning And Neural Networks easily. We will develop Keras / TensorFlow Deep Learning Models using GUI and without knowing Python or programming. If you are a python programmer, in this course you will learn a much easier and faster way to develop and deploy Keras / TensorFlow machine learning models. You will learn about important machine learning concepts such as datasets, test set splitting, deep neural networks, normailzation, dropout, artificial networks, neural network models, hyperparameters, WITHOUT hard and boring technical explanations or math formulas, or follow along code. Instead, you will learn these concepts from practical and easy to follow along teaching methods. In this course, Deep Learning Studio will produce all the python code for you in the backend, and you never even have to even look at it (unless of course you want to). By the end of this course you will be able to build, train and deploy deep learning AI models without having to do any coding. After taking this course you will be able to produce well written professional python code without even knowing what python is or how to program, Deep Learning Studio will do all this work for you. Instead you can easily stay focused on building amazing artificial intelligence machine learning solutions without programming. Also, if you just want to learn more about Deep Learning Studio and get a jump start on this revolutionary ststem, this is the course for you! Deep Learning Studio is just beginning to shake up the data science world and how artificial intelligence solutions are developed! Get ahead of the curve by taking this exciting and easy to follow along course! | https://www.udemy.com/course/build-neural-networks-in-seconds-using-deep-learning-studio/#instructor-1 | Hello There! I'm Michael Kroeker and I'm very glad you are here! I'm a technologist and data science consultant and service provider. Starting in 6th grade I had a driving passion for technology and science, and eventually I went on to graduate from college as a technologist. Beginning in 1993 my career began as a digital radio engineer and telecontrol technician at a major state power corporation in North America. My focus soon changed from communication equipment to software system development for monitoring transmission quality. Following relocation to Sweden, I became a consultant for 3G mobile network rollout and I was involved in systems software. After the completion of this phase, my career continued to evolve as I became involved as a data scientist and developer after being recruited by a binary options algorithmic trading department. Eventually I moved on to become a developer for algorithmic forex trading systems. I am as passionate as ever for technology and science, and I look forward to sharing my knowledge, expertise and experience with you! | Deep Learning | Data Scientist | >=3 | Below 1K | Below 1K | >=3 | Below 1 K | Below 1 K | |||||||||||||||||
Complete Machine Learning & Data Science with Python | A-Z | Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science. | 4.7 | 180 | 17786 | Created by Oak Academy | Nov-22 | English | $9.99 | 9h 25m total length | https://www.udemy.com/course/complete-machine-learning-data-science-with-python-a-z/ | Oak Academy | Web & Mobile Development, IOS, Android, Ethical Hacking, IT | 4.5 | 26588 | 264649 | Hello there, Welcome to the “Complete Machine Learning & Data Science with Python | A-Z” course. Python, machine learning, django, python programming, machine learning python, python for beginners, data science Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn, and dive into machine learning A-Z with Python and Data Science. Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, my course on OAK Academy here to help you apply machine learning to your work. Complete machine learning & data science with python | a-z, machine learning a-z, Complete machine learning & data science with python, complete machine learning and data science with python a-z, machine learning using python, complete machine learning and data science, machine learning, complete machine learning, data science It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models. Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks. Do you know data science needs will create 11.5 million job openings by 2026? Do you know the average salary is $100.000 for data science careers! Data Science Careers Are Shaping The Future Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. If you want to learn one of the employer’s most request skills? If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python? If you are an experienced developer and looking for a landing in Data Science! In all cases, you are at the right place! We've designed for you “Complete Machine Learning & Data Science with Python | A-Z” a straightforward course for Python Programming Language and Machine Learning. In the course, you will have down-to-earth way explanations with projects. With this course, you will learn machine learning step-by-step. I made it simple and easy with exercises, challenges, and lots of real-life examples. We will open the door of the Data Science and Machine Learning a-z world and will move deeper. You will learn the fundamentals of Machine Learning A-Z and its beautiful libraries such as Scikit Learn. Throughout the course, we will teach you how to use Python to analyze data, create beautiful visualizations, and use powerful machine learning python algorithms. This Machine Learning course is for everyone! My "Machine Learning with Hands-On Examples in Data Science" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher). Why we use a Python programming language in Machine learning? Python is a general-purpose, high-level, and multi-purpose programming language. The best thing about Python is, it supports a lot of today’s technology including vast libraries for Twitter, data mining, scientific calculations, designing, back-end server for websites, engineering simulations, artificial learning, augmented reality and what not! Also, it supports all kinds of App development. What you will learn? In this course, we will start from the very beginning and go all the way to the end of "Machine Learning" with examples. Before each lesson, there will be a theory part. After learning the theory parts, we will reinforce the subject with practical examples. During the course you will learn the following topics: What is Machine Learning? More About Machine Learning Machine Learning Terminology Evaluation Metrics What is Classification vs Regression? Evaluating Performance-Classification Error Metrics Evaluating Performance-Regression Error Metrics Machine Learning with Python Supervised Learning Cross-Validation and Bias Variance Trade-Off Use Matplotlib and seaborn for data visualizations Machine Learning with SciKit Learn Linear Regression Theory Logistic Regression Theory Logistic Regression with Python K Nearest Neighbors Algorithm Theory K Nearest Neighbors Algorithm With Python K Nearest Neighbors Algorithm Project Overview K Nearest Neighbors Algorithm Project Solutions Decision Trees And Random Forest Algorithm Theory Decision Trees And Random Forest Algorithm With Python Decision Trees And Random Forest Algorithm Project Overview Decision Trees And Random Forest Algorithm Project Solutions Support Vector Machines Algorithm Theory Support Vector Machines Algorithm With Python Support Vector Machines Algorithm Project Overview Support Vector Machines Algorithm Project Solutions Unsupervised Learning Overview K Means Clustering Algorithm Theory K Means Clustering Algorithm With Python K Means Clustering Algorithm Project Overview K Means Clustering Algorithm Project Solutions Hierarchical Clustering Algorithm Theory Hierarchical Clustering Algorithm With Python Principal Component Analysis (PCA) Theory Principal Component Analysis (PCA) With Python Recommender System Algorithm Theory Recommender System Algorithm With Python Complete machine learning Python machine learning Machine learning a-z With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions. What is machine learning? Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model. What is machine learning used for? Machine learning a-z is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing, and more. In any situation in which pattern recognition, prediction, and analysis are critical, machine learning can be of use. Machine learning is often a disruptive technology when applied to new industries and niches. Machine learning engineers can find new ways to apply machine learning technology to optimize and automate existing processes. With the right data, you can use machine learning technology to identify extremely complex patterns and yield highly accurate predictions. Does Machine learning require coding? It's possible to use machine learning data science without coding, but building new systems generally requires code. For example, Amazon’s Rekognition service allows you to upload an image via a web browser, which then identifies objects in the image. This uses a pre-trained model, with no coding required. However, developing machine learning systems involves writing some Python code to train, tune, and deploy your models. It's hard to avoid writing code to pre-process the data feeding into your model. Most of the work done by a machine learning practitioner involves cleaning the data used to train the machine. They also perform “feature engineering” to find what data to use and how to prepare it for use in a machine learning model. Tools like AutoML and SageMaker automate the tuning of models. Often only a few lines of code can train a model and make predictions from it What is the best language for machine learning? Python is the most used language in machine learning using python. Engineers writing machine learning systems often use Jupyter Notebooks and Python together. Jupyter Notebooks is a web application that allows experimentation by creating and sharing documents that contain live code, equations, and more. Machine learning involves trial and error to see which hyperparameters and feature engineering choices work best. It's useful to have a development environment such as Python so that you don't need to compile and package code before running it each time. Python is not the only language choice for machine learning. Tensorflow is a popular framework for developing neural networks and offers a C++ API. There is a complete machine learning framework for C# called ML. NET. Scala or Java are sometimes used with Apache Spark to build machine learning systems that ingest massive data sets. What are the different types of machine learning? Machine learning is generally divided between supervised machine learning and unsupervised machine learning. In supervised machine learning, we train machine learning models on labeled data. For example, an algorithm meant to detect spam might ingest thousands of email addresses labeled 'spam' or 'not spam.' That trained model could then identify new spam emails even from data it's never seen. In unsupervised learning, a machine learning model looks for patterns in unstructured data. One type of unsupervised learning is clustering. In this example, a model could identify similar movies by studying their scripts or cast, then group the movies together into genres. This unsupervised model was not trained to know which genre a movie belongs to. Rather, it learned the genres by studying the attributes of the movies themselves. There are many techniques available within. Is Machine learning a good career? Machine learning python is one of the fastest-growing and popular computer science careers today. Constantly growing and evolving, you can apply machine learning to a variety of industries, from shipping and fulfillment to medical sciences. Machine learning engineers work to create artificial intelligence that can better identify patterns and solve problems. The machine learning discipline frequently deals with cutting-edge, disruptive technologies. However, because it has become a popular career choice, it can also be competitive. Aspiring machine learning engineers can differentiate themselves from the competition through certifications, boot camps, code repository submissions, and hands-on experience. What is the difference between machine learning and artifical intelligence? Machine learning is a smaller subset of the broader spectrum of artificial intelligence. While artificial intelligence describes any "intelligent machine" that can derive information and make decisions, machine learning describes a method by which it can do so. Through machine learning, applications can derive knowledge without the user explicitly giving out the information. This is one of the first and early steps toward "true artificial intelligence" and is extremely useful for numerous practical applications. In machine learning applications, an AI is fed sets of information. It learns from these sets of information about what to expect and what to predict. But it still has limitations. A machine learning engineer must ensure that the AI is fed the right information and can use its logic to analyze that information correctly. What skills should a machine learning engineer know? A python machine learning engineer will need to be an extremely competent programmer with in-depth knowledge of computer science, mathematics, data science, and artificial intelligence theory. Machine learning engineers must be able to dig deep into complex applications and their programming. As with other disciplines, there are entry-level machine learning engineers and machine learning engineers with high-level expertise. Python and R are two of the most popular languages within the machine learning field. What is python? Machine learning python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python bootcamp is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks. Python vs. R: What is the Difference? Python and R are two of today's most popular programming tools. When deciding between Python and R in data science , you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance. What does it mean that Python is object-oriented? Python is a multi-paradigm language, which means that it supports many data analysis programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping. What are the limitations of Python? Python is a widely used, general-purpose programming language, but it has some limitations. Because Python in machine learning is an interpreted, dynamically typed language, it is slow compared to a compiled, statically typed language like C. Therefore, Python is useful when speed is not that important. Python's dynamic type system also makes it use more memory than some other programming languages, so it is not suited to memory-intensive applications. The Python virtual engine that runs Python code runs single-threaded, making concurrency another limitation of the programming language. Though Python is popular for some types of game development, its higher memory and CPU usage limits its usage for high-quality 3D game development. That being said, computer hardware is getting better and better, and the speed and memory limitations of Python are getting less and less relevant. How is Python used? Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks in the background. Many of the scripts that ship with Linux operating systems are Python scripts. Python is also a popular language for machine learning, data analytics, data visualization, and data science because its simple syntax makes it easy to quickly build real applications. You can use Python to create desktop applications. Many developers use it to write Linux desktop applications, and it is also an excellent choice for web and game development. Python web frameworks like Flask and Django are a popular choice for developing web applications. Recently, Python is also being used as a language for mobile development via the Kivy third-party library. What jobs use Python? Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website and server deployments. Web developers use Python to build web applications, usually with one of Python's popular web frameworks like Flask or Django. Data scientists and data analysts use Python to build machine learning models, generate data visualizations, and analyze big data. Financial advisors and quants (quantitative analysts) use Python to predict the market and manage money. Data journalists use Python to sort through information and create stories. Machine learning engineers use Python to develop neural networks and artificial intelligent systems. How do I learn Python on my own? Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own. What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us. We need to interpret the information and discover hidden patterns. This is where data science comes in. Data science uses algorithms to understand raw data. The main difference between data science and traditional data analysis is its focus on prediction. Data science seeks to find patterns in data and use those patterns to predict future data. It draws on machine learning to process large amounts of data, discover patterns, and predict trends. Data science includes preparing, analyzing, and processing data. It draws from many scientific fields, and as a science, it progresses by creating new algorithms to analyze data and validate current methods. What does a data scientist do? Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. This requires several steps. First, they must identify a suitable problem. Next, they determine what data are needed to solve such a situation and figure out how to get the data. Once they obtain the data, they need to clean the data. The data may not be formatted correctly, it might have additional unnecessary data, it might be missing entries, or some data might be incorrect. Data Scientists must, therefore, make sure the data is clean before they analyze the data. To analyze the data, they use machine learning techniques to build models. Once they create a model, they test, refine, and finally put it into production. What are the most popular coding languages for data science? Python is the most popular programming language for data science. It is a universal language that has a lot of libraries available. It is also a good beginner language. R is also popular; however, it is more complex and designed for statistical analysis. It might be a good choice if you want to specialize in statistical analysis. You will want to know either Python or R and SQL. SQL is a query language designed for relational databases. Data scientists deal with large amounts of data, and they store a lot of that data in relational databases. Those are the three most-used programming languages. Other languages such as Java, C++, JavaScript, and Scala are also used, albeit less so. If you already have a background in those languages, you can explore the tools available in those languages. However, if you already know another programming language, you will likely be able to pick up Python very quickly. How long does it take to become a data scientist? This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. It will also depend on your starting place. If you already have a strong base in mathematics and statistics, you will have less to learn. If you have no background in statistics or advanced mathematics, you can still become a data scientist; it will just take a bit longer. Data science requires lifelong learning, so you will never really finish learning. A better question might be, "How can I gauge whether I know enough to become a data scientist?" Challenge yourself to complete data science projects using open data. The more you practice, the more you will learn, and the more confident you will become. Once you have several projects that you can point to as good examples of your skillset as a data scientist, you are ready to enter the field. How can I learn data science on my own? It is possible to learn data science on your own, as long as you stay focused and motivated. Luckily, there are a lot of online courses and boot camps available. Start by determining what interests you about data science. If you gravitate to visualizations, begin learning about them. Starting with something that excites you will motivate you to take that first step. If you are not sure where you want to start, try starting with learning Python. It is an excellent introduction to programming languages and will be useful as a data scientist. Begin by working through tutorials or Udemy courses on the topic of your choice. Once you have developed a base in the skills that interest you, it can help to talk with someone in the field. Find out what skills employers are looking for and continue to learn those skills. When learning on your own, setting practical learning goals can keep you motivated. Does data science require coding? The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to code, but others disagree. A lot of algorithms have been developed and optimized in the field. You could argue that it is more important to understand how to use the algorithms than how to code them yourself. As the field grows, more platforms are available that automate much of the process. However, as it stands now, employers are primarily looking for people who can code, and you need basic programming skills. The data scientist role is continuing to evolve, so that might not be true in the future. The best advice would be to find the path that fits your skillset. What skills should a data scientist know? A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which are essential pillars of data science. A good understanding of these concepts will help you understand the basic premises of data science. Familiarity with machine learning is also important. Machine learning is a valuable tool to find patterns in large data sets. To manage large data sets, data scientists must be familiar with databases. Structured query language (SQL) is a must-have skill for data scientists. However, nonrelational databases (NoSQL) are growing in popularity, so a greater understanding of database structures is beneficial. The dominant programming language in Data Science is Python — although R is also popular. A basis in at least one of these languages is a good starting point. Finally, to communicate findings, data scientists require knowledge of visualizations. Data visualizations allow them to share complex data in an accessible manner. Is data science a good career? The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators, and analytics managers. The jobs also generally pay well. This might make you wonder if it would be a promising career for you. A better understanding of the type of work a data scientist does can help you understand if it might be the path for you. First and foremost, you must think analytically. Data science is about gaining a more in-depth understanding of info through data. Do you fact-check information and enjoy diving into the statistics? Although the actual work may be quite technical, the findings still need to be communicated. Can you explain complex findings to someone who does not have a technical background? Many data scientists work in cross-functional teams and must share their results with people with very different backgrounds. If this sounds like a great work environment, then it might be a promising career for you. What does it mean that Python is object-oriented? Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles, Python also supports the object-oriented style of programming. In object-oriented programming, a developer completes a programming project by creating Python objects in code that represent objects in the actual world. These objects can contain both the data and functionality of the real-world object. To generate an object in Python you need a class. You can think of a class as a template. You create the template once, and then use the template to create as many objects as you need. Python classes have attributes to represent data and methods that add functionality. A class representing a car may have attributes like color, speed, and seats and methods like driving, steering, and stopping. The concept of combining data with functionality in an object is called encapsulation, a core concept in the object-oriented programming paradigm. Why would you want to take this course? Our answer is simple: The quality of teaching. OAK Academy based in London is an online education company. OAK Academy gives education in the field of IT, Software, Design, development in English, Portuguese, Spanish, Turkish, and a lot of different languages on the Udemy platform where it has over 1000 hours of video education lessons. OAK Academy both increases its education series number by publishing new courses, and it makes students aware of all the innovations of already published courses by upgrading. When you enroll, you will feel the OAK Academy`s seasoned developers' expertise. Questions sent by students to our instructors are answered by our instructors within 48 hours at the latest. Video and Audio Production Quality All our videos are created/produced as high-quality video and audio to provide you the best learning experience. You will be, Seeing clearly Hearing clearly Moving through the course without distractions You'll also get: Lifetime Access to The Course Fast & Friendly Support in the Q&A section Udemy Certificate of Completion Ready for Download We offer full support, answering any questions. If you are ready to learn the “Complete Machine Learning & Data Science with Python | A-Z” course. Dive in now! See you in the course! | https://www.udemy.com/course/complete-machine-learning-data-science-with-python-a-z/#instructor-1 | Hi there, By 2024, there will be more than 1 million unfilled computing jobs and the skills gap is a global problem. This was our starting point. At OAK Academy, we are the tech experts who have been in the sector for years and years. We are deeply rooted in the tech world. We know the tech industry. And we know the tech industry's biggest problem is the “tech skills gap” and here is our solution. OAK Academy will be the bridge between the tech industry and people who -are planning a new career -are thinking career transformation -want career shift or reinvention, -have the desire to learn new hobbies at their own pace Because we know we can help this generation gain the skill to fill these jobs and enjoy happier, more fulfilling careers. And this is what motivates us every day. We specialize in critical areas like cybersecurity, coding, IT, game development, app monetization, and mobile. Thanks to our practical alignment we are able to constantly translate industry insights into the most in-demand and up-to-date courses, OAK Academy will provide you the information and support you need to move through your journey with confidence and ease. Our courses are for everyone. Whether you are someone who has never programmed before, or an existing programmer seeking to learn another language, or even someone looking to switch careers we are here. OAK Academy here to transforms passionate, enthusiastic people to reach their dream job positions. If you need help or if you have any questions, please do not hesitate to contact our team. | Machine Learning | >=4 | Below 1K | >=15K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Tidy Data: Updated Data Processing With tidyr and dplyr in R | Learn Data Preprocessing, Data Wrangling and Data Visualisation With the Two Most Happening R Data Science Packages | 4.4 | 180 | 1284 | Created by Minerva Singh | Oct-22 | English | $9.99 | 4h 33m total length | https://www.udemy.com/course/tidy-data-updated-data-processing-with-tidyr-and-dplyr-in-r/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | THIS IS YOUR ROADMAP TO LEARNING & BECOMING HIGHLY PROFICIENT IN DATA PREPROCESSING, DATA WRANGLING, & DATA VISUALIZATION USING TWO OF THE MOST IN-DEMAND R DATA SCIENCE PACKAGES! Hello, My name is Minerva Singh. I am an Oxford University MPhil graduate in Geography & Environment & I finished a PhD at Cambridge University in Tropical Ecology & Conservation. I have +5 of experience in analysing real-life data from different sources using statistical modelling and producing publications for international peer-reviewed journals. If you find statistics books & manuals too vague, expensive & not practical, then you’re going to love this course! I created this course to take you by hand and teach you all the concepts, and tackle the most fundamental building block on practical data science - data wrangling and visualisation. THIS COURSE WILL TEACH YOU ALL YOU NEED AND PUT YOUR KNOWLEDGE TO PRACTICE NOW! This course is your sure-fire way of acquiring the knowledge and statistical data analysis wrangling and visualisation skills that I acquired from the rigorous training I received at 2 of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in the renowned international journal like PLOS One. HERE IS WHAT THIS COURSE WILL DO FOR YOU: It will take you (even if you have no prior statistical modelling/analysis background) from a basic level of performing some of the most common data wrangling tasks in R- with two of the most happening R data science packages tidyverse and dplyr. It will equip you to use some of the most important R data wrangling and visualisation packages such as dplyr and ggplot2. It will Introduce some of the most important data visualisation concepts to you in a practical manner such that you can apply these concepts for practical data analysis and interpretation. You will also be able to decide which wrangling and visualisation techniques are best suited to answer your research questions and applicable to your data and interpret the results.. The course will mostly focus on helping you implement different techniques on real-life data such as Olympic medal winners After each video, you will learn a new concept or technique which you may apply to your own projects immediately! Reinforce your knowledge through practical quizzes and assignments. ON TOP OF THE COURSE, I’M ALSO OFFERING YOU: Practice Activities To Reinforce Your Learning My Continuous Support To Make Sure You Gain Complete Understanding & Proficiency Access To Future Course Updates Free Of Charge I’ll Even Go The Extra Mile & Cover Any Topics That Are Related To The Subject That You Need Help With (This is something you can’t get anywhere else). & Access To A Community Of 25,000 Data Scientists (& growing) All Learning Together & Helping Each Other! Now, go ahead & enrol in the course. I’m certain you’ll love it, but in case you don’t, you can always request a refund within 30 days. No hard feelings whatsoever. I look forward to seeing you inside! | https://www.udemy.com/course/tidy-data-updated-data-processing-with-tidyr-and-dplyr-in-r/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Misc | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Pandas Masterclass 2022: Advanced Data Analysis with Pandas | Master Pandas library to Analyze, Manipulate & Visualize Big Data. Data Analysis with Pandas using Multiple Projects | 4.8 | 179 | 22221 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 8h 58m total length | https://www.udemy.com/course/pandas-python/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Welcome to Pandas Masterclass: Advanced Data Analysis and Visualisation with Pandas. Pandas is a fast, flexible, easy-to-use open-source data analysis and data manipulation library built on top of the python programming language. It offers data structures and many operations for manipulating data. Pandas allow many data manipulation operations such as merging, reshaping, cleaning, and data wrangling features. Pandas library is widely used for data science/data analysis and machine learning tasks. If you want to master the data analysis library-pandas then this course is perfect for you. You will gain complete knowledge of Pandas library. From reading datasets to analyzing them using the different manipulation functions. Everything is included in this course. In this course, you will cover the following topics:- Reading different file formats using Pandas. General functions of pandas. Handling Series using Pandas. Handling Dataframe using Pandas. Introduction to Arrays in Pandas Handling time data using Pandas. Styling data frame using Pandas. Window functions using Pandas. Visualization and plotting using Pandas. Index Objects in Pandas. This course is made by keeping in mind the requirements of data analysts and data scientists while working on any project. After completing this course you will be able to work on any data analysis project efficiently using pandas. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! You will definitely love this course. You will have access to all the resources used in this course. Enroll now and become a pandas pro!!! | https://www.udemy.com/course/pandas-python/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Misc | Grandmaster | >=4 | Below 1K | >=20K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Advanced Data Science Techniques in SPSS | Hone your SPSS skills to perfection - grasp the most high level data analysis methods available in the SPSS program. | 4.7 | 177 | 25209 | Created by Bogdan Anastasiei | Dec-20 | English | $9.99 | 6h 41m total length | https://www.udemy.com/course/advanced-data-science-techniques-in-spss/ | Bogdan Anastasiei | University Teacher and Consultant | 4.5 | 7750 | 296607 | Become a Top Performing Data Analyst – Take This Advanced Data Science Course in SPSS! Within a few days only you can master some of the most complex data analysis techniques available in the SPSS program. Even if you are not a professional mathematician or statistician, you will understood these techniques perfectly and will be able to apply them in practical, real life situations. These methods are used every day by data scientists and data miners to make accurate predictions using their raw data. If you want to be a high skilled analyst, you must know them! Without further ado, let’s see what you are going to learn… Stepwise regression analysis, a technique that helps you select the best subset of predictors for a regression analysis, when you have a big number of predictors. This way you can create regression models that are both parsimonious and effective. Nonlinear regression analysis. After finishing this course, you will be able to fit any nonlinear regression model using SPSS. K nearest neighbor, a very popular predictive technique used mostly for classification purposes. So you will learn how to predict the values of a categorical variable with this method. Decision trees. We will approach both binary (CART) and non-binary (CHAID) trees. For each of these two types we will consider two cases: the case of response dependent variables (regression trees) and the case of categorical response variables (classification trees). Neural networks. Artificial neural networks are hot now, since they are a suitable predictive tool in many situations. In SPSS we can train two types of neural network: the multilayer perceptron (MLP) and the radial basis function (RBF) network. We are going to study both of them in detail. Two-step cluster analysis, an effective grouping procedure that allows us to identify homogeneous groups in our population. It is useful in very many fields like marketing research, medicine (gene research, for example), biology, computer science, social science etc. Survival analysis. If you have to estimate one of the following: the probable time until a certain event happens, what percentage of your population will suffer the event or which particular circumstances influence the probability that the event happens, than you need to apply on of the survival analysis method studied here: Kaplan-Meier or Cox regression. For each analysis technique, a short theoretical introduction is provided, in order to familiarize the reader with the fundamental notions and concepts related to that technique. Afterwards, the analysis is executed on a real-life data set and the output is thoroughly explained. Moreover, for some techniques (KNN, decision trees, neural networks) you will also learn: How to validate your model on an independent data set, using the validation set approach or the cross-validation How to save the model and use it for make predictions on new data that may be available in the future. Join right away and start building sophisticated, in-demand data analysis skills in SPSS! | https://www.udemy.com/course/advanced-data-science-techniques-in-spss/#instructor-1 | My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have 24 years experience in teaching and about 15 years experience in business consulting. | Misc | Consultant | >=4 | Below 1K | >=25K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Spark Scala coding framework, testing, Structured streaming | Spark Scala Framework, Hive, IntelliJ, Maven, Logging, Exception Handling, log4j, ScalaTest, JUnit, Structured Streaming | 4.4 | 177 | 3239 | Created by FutureX Skills | Nov-21 | English | $10.99 | 4h 24m total length | https://www.udemy.com/course/spark-scala-coding-best-practices-data-pipeline/ | FutureX Skills | Data AI evangelists | 4.3 | 2683 | 45656 | This course will bridge the gap between your academic and real world knowledge and prepare you for an entry level Big Data Spark Scala developer role. You will learn the following Spark Scala coding best practices Logging - log4j, slf4 Exception Handling Configuration using Typesafe config Doing development work using IntelliJ, Maven Using your local environment as a Hadoop Hive environment Reading and writing to a Postgres database using Spark Unit Testing Spark Scala using JUnit , ScalaTest, FlatSpec & Assertion Building a data pipeline using Hadoop , Spark and Postgres Bonus - Setting up Cloudera QuickStart VM on Google Cloud Platform (GCP) Structured Streaming Prerequisites : Basic programming skills Basic database knowledge Big Data and Spark entry level knowledge | https://www.udemy.com/course/spark-scala-coding-best-practices-data-pipeline/#instructor-1 | We are a group of Solution Architects and Developers with expertise in Java, Python, Scala , Big Data , Machine Learning and Cloud. We have years of experience in building Data and Analytics solutions for global clients. Our primary goal is to simplify learning for our students. We take a very practical use case based approach in all our courses. | Scala | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Jetson Nano Boot Camp | Learn Jetson Nano with Machine Learning Project, Python, OpenCV and Serial Communication! | 4.6 | 177 | 682 | Created by Yılmaz Alaca | May-22 | English | $9.99 | 6h 0m total length | https://www.udemy.com/course/jetson-nano-boot-camp/ | Yılmaz Alaca | Elektrik Elektronik Mühendisi | 4.6 | 1143 | 8438 | Hello everyone, Welcome to the introduction of my Jetson Nano Boot Camp course. Nowadays, image processing, computer vision and Python programming language are becoming very popular. In order to realize our own machine learning projects, we will carry out a machine learning project with Jetson Nano which is a powerful artificial intelligence computer. Not limited to this, we will learn about Python, Image Processing and machine learning. Of course, we will start with Python and learn the basics of Python well.We will learn about the concept of variables, standard input and output functions, loops, conditional structures, functions, modules and more. And then we will get into the topic of image processing. We will learn the basics of the OpenCV library, which has been developed for a long time, and detect the objects we want in real time with the software using the machine learning method. Of course, in addition to detecting objects, we will also be able to control the Arduino board, which is very popular in the world, according to the object we have detected. Thanks to the school crossing sign project included in the course, we will detect our school crossing sign together with our Jetson Nano board and then reduce its speed according to the school crossing sign where we detect our DC motor that we connect to our Arduino board. Since the course was created as a result of years of academic and technical experience, you will be able to carry out your own machine learning projects together with Jetson Nano when you finish the course. No previous programming or electronics knowledge is required. "You are never too old to set another goal or to dream a new dream." - C.S.Lewis "Do the difficult things while they are easy and do the great things while they are small. A journey of a thousand miles begins with a single step" - Lao Tzu You get the best information that I have compiled over years of trial and error and experience! Best wishes, Yılmaz ALACA | https://www.udemy.com/course/jetson-nano-boot-camp/#instructor-1 | TR -Yaklaşık 7 yıldır C++ yazılım dili ve elektronik kartlar üzerinde uğraşmaktayım. Elektrik Elektronik Mühendisi lisans öğrencisiyim ve üniversitemde kurmuş olduğum topluluğumda yazılım ve elektronik dersleri veriyorum. -------------------------------------------------------------- EN- I've strived on C++ and electronic boards for 7 years. I study at university and my department is Electronic engineer. I'm teaching software and electronic lessons at my club. | Misc | >=4 | Below 1K | Below 1K | >=4 | Below 10 K | Below 10 K | ||||||||||||||||||
4x1 Data Management/Governance/Security/Ethics Masterclass | You'll learn about data literacy, Data Quality operations, Data Governance policies and Data Security/Privacy controls. | Bestseller | 4.4 | 177 | 3938 | Created by Vasco Patrício | Nov-22 | English | $9.99 | 15h 22m total length | https://www.udemy.com/course/data-management-and-governance/ | Vasco Patrício | Executive Coach, Past MIT Portugal IEI-Backed Founder | 4.4 | 4068 | 108806 | MANAGE YOUR KNOWLEDGE, MANAGE YOUR DATA There are many activities related to data in organisations. Data Management (DM), Data Governance (DG), Data Stewardship, Data Science, and many others. All of these are crucial activities for organisations, especially those trying to protect their data from cyberattacks, complying with regulation, or just trying to improve the quality of their analytics and reports. Frequently, you can find information on one of these activities, but not all. And on top of that, many courses use different definitions, so you may become confused. In short, most courses on data don't fit the minimum requirements. And this has consequences not just for your career, but yourself personally as well. What happens when you don't have enough information (or in the adequate format)? You'll become confused by the myriad data activities, the tools used, which roles and responsibilities each person has, and how they intersect; You won't be able to properly identify what belongs to Data Management or what belongs to Data Governance - and what should not be done at all; You'll become frustrated and irritated that you don't know why an operation works, or why it doesn't; You won't be able to identify what a specific data tool should be used for, and when your current tools don't fit the job; You won't know how to optimize your DM and DG operations in an organisation, resulting people not taking data seriously, or making obvious mistakes; You won't be able to know what security controls to apply to what specific data classes, or how to protect data subjects; So if you want to know everything about Data Management, Data Governance, Data Security and a lot more, what is my proposed solution? This new course masterclass, of course! A HIGH-QUALITY COURSE FOR HIGH-QUALITY DATA Unlike other data management or data governance courses you'll find out there, this course is comprehensive and updated. In other words, not only did I make sure that you'll find more topics (and more in-depth) than other courses you may find, but I also made sure to keep the information relevant to the types of data quality issues you'll find nowadays. Data operations may seem complex by nature, but they rely on simple principles. In this course, you'll learn about the essentials of how data are managed with activities such as profiling and remediation, as well as how data are governed with processes and policies. Not only that, we'll dive deep into the activities, stakeholders, projects and resources that each discipline entails. In this 4-hour+ masterclass, you'll find the following modules: You'll learn about the essential Data Literacy and Considerations (what are the key principles, what are the different data disciplines, usual processes of each, the information lifecycle, and sophistication levels in an organisation); You'll get to know about Data and Data Quality in specific (including the types of data that exist, the types of data quality issues and their financial impact, the Data Management activity process flow, as well as the data dimensions and tools used); You'll learn about Data Governance (including how to define principles and policies, what are the specific activities such as data classification, data lineage tracking and more, as well as how to implement DG from scratch in an organisation); You'll learn about Data Security, Privacy and Ethics (including what security and privacy controls to use to protect data both in physical and logical formats, how to ensure that data subjects are treated fairly by algorithms and across geographies, how to implement and measure data ethics, and more); By the end of this course, you will know exactly how data are managed and how they are governed in an organisation, to a deep level, including the necessary tools, people, and activities. The best of this masterclass? Inside you'll find these 4 modules. In short, even if you only fit one of the three profiles (only Data Management, only Data Governance, only Data Security/Ethics, or only "general" Data Quality knowledge), you will still have a course dedicated to it! And naturally, if you are interested in multiple of these topics... this is the ultimate package for you. THE PERFECT COURSE... FOR WHOM? This course is targeted at different types of people. Naturally, if you're a current or future data professional, you will find this course useful, as well as if you are any other professional or executive involved in a data project in your organisation. But even if you're any other type of professional that aims to know more about how data work, you'll find the course useful. More specifically, you're the ideal student for this course if: You're someone who wants to know more about data management itself (how to profile datasets, how to parse/cleanse/standardise them, how to link and merge records, or how to enhance data); You're someone who is interested in data governance (how to define rules and controls for data, how to institute policies, how to define required metadata, how to fill said metadata for different data sources, and many other activities); You're some who is interested in data security and privacy, or in data ethics (that is, how to ensure that data subjects are protected in terms of data safety, but also in terms of actually being treated fairly by processes and algorithms); You're someone who wants to know more about data quality in general (what are the usual types of problems, how do they create financial impact in organisations, what are the usual activities to improve DQ, and so on); LET ME TELL YOU... EVERYTHING Some people - including me - love to know what they're getting in a package. And by this, I mean, EVERYTHING that is in the package. So, here is a list of everything that this masterclass covers: Data Literacy and Fundamentals You'll learn about the 4 key principles for any successful data initiative - considering data at assets, monetising them, seeing DG as business and not IT, and gauging your organisation's sophistication level; You'll learn about the key data disciplines - that is, what is Data Management (DM), what is Data Governance (DG), what is Data Stewardship, and other activities such as Data Science, and terms such as Data Quality (DQ), as well as the specific roles and operations related to each of these in specific; You'll learn about the main activities in DM and DG. In the case of DM, activities such as profiling data, remediating them, and setting future data validity requirements, and in the case of DG, uncovering business rules, setting policies and expectations for data, and controls to measure DQ, among others; You'll get to know the different stages of the information lifecycle. Data being created, accessed, changed, deleted, and possibly other intermediate steps, as well as the usual preoccupations and controls at each stage; You'll learn about the usual progression from projects to processes - how both DM and DG usually start as specific projects with local scope, and usually grow within an organisation, culminating in replicable and centralised processes to manage and govern data; You'll get to know the possible sophistication levels of an organisation in terms of managing data. Being reactive, with no allocated tools or people, versus having centralised and standardised roles, tools and processes for data operations, and gauging your organisation; Data and Data Quality Management: You'll get to know the 4 main types of data. Master data, reference data, transactional data and metadata, as well as the nuances of each and how they intersect; You'll learn about the types of DQ issues and their financial impact, usually in one of 3 main ways: direct costs, operational inefficiencies, and/or compliance or regulatory sanctions; You'll learn about the usual DQ improvement process, starting with profiling, usually followed by triage, remediation of the data, and possible setup of automated controls to prevent future errors; You'll learn about the three main types of DQ actions. Remediating data on the spot, analyzing the root cause of data problems, and/or instituting rules with automated controls to measure/prevent future data problems; You'll get to know the different data dimensions used when analyzing DQ problems. Completeness, accuracy, timeliness, lineage, and other relevant ones; You'll know more about the effect of Big Data and/or AI in data management, specifically the consequences both have in terms of the remediation possibilities and the data pipelines; You'll know more about the tools used for DQ management, including profiling, parsing and standardisation, linking and merging, and data enhancement tools; You'll get to know data profiling tools and their specific uses, including validating values in datasets, detecting outliers, validating data formats and rules, and/or uncovering implicit business rules; You'll learn more about parsing and standardisation tools, which usually take data in different formats, parse them into a unified format, and then standardise data in that format, including the possible removal/editing of wrong values ("cleansing"); You'll get to know linking and merging tools, used to prevent duplicates, which usually use a comparison algorithm to establish a match between records, as being the same, which can then be merged; You'll learn about data enhancement and annotation tools, which allow you to add more data to the current data, when these can't be edited - or don't need to be edited; You'll learn more about building a business case for DM/DG, including the usual operations and steps, the usual costs and savings mentioned, and how to present it; Data Governance: You'll learn about the common functions and capabilities enabled by DG, from privacy and security controls to lineage tracking, metadata management, data classification, monitoring and more; You'll learn about the usual roles and responsibilities in DG, from data owners to data stewards, Accountable Executives, who is the Data Council, the differences between the CDO (Chief Data Officer) and CIO (Chief Information Officer), and others; You'll learn about data classification, including the 3 major systems used (by priority and criticality, by sensitivity and privacy requirements, and by processing stage), as well as the consequences of each; You'll learn about what is data stewardship, and how it bridges Data Governance and reality, including multiple tasks related to metadata, master data, reference data, tracking DQ issues, and other activities, as well as the differences between business, technical and operational data stewards; You'll learn about a day in the life in Data Governance, including the tasks that each role performs, how they coordinate, and how decisions are made; You'll learn about assessing your organisation in preparation to implement DG, including data management maturity assessments and change capacity assessments, and how to define the scope of a DG program selecting processes, people and tools; You'll learn about how to engage and obtain buy-in from different stakeholders, how to deal with resistance, how to prioritise the available projects, and how to ensure commitment to DG; You'll learn about how to architect and define the initial DG program, including the tools used, the data lifecycle stages, the roles and responsibilities involved, and how to bring it all together in a single operating model; You'll learn about how to deploy DG initially, with a roadmap, milestones, an operating model, and metrics to track; You'll learn about the possible initiatives complementary to DG, including data-centric projects such as MDM (Master Data Management) or EIM (Enterprise Information Management), analytics and AI projects, and big ERP implementations, as well as how to let DG "piggyback" on these; You'll learn about how to maintain a DG program, including maintaining key processes such as training and communication, while enforcing behavioral change through change management and controls; You'll learn about how to scale a DG program, including dealing with resistance by new stakeholders from new projects and defining the federation of DG as it grows within the organisation; You'll learn about how culture affects DG, including how aspects such as data literacy, commitment and others define the resources and the people accountable for DG; Data Security, Privacy and Ethics You'll learn about cryptographic protection (how to protect data with encryption), and measures to take into account; You'll learn about data retention and disposal - that is, how to minimise retention time for data which may be leaked, as well as dispose of them securely; You'll learn about locked rooms, locked devices and/or locked ports, placing physical barriers in front of attackers trying to steal data; You'll learn about physical media protection, that is, how to protect media such as HDDs, USB drives, or paper while at rest, in transit and during their destruction; You'll learn how service provider assessment and monitoring is done, to prevent third parties from introducing vulnerabilities in your organisation; You'll learn about how geographical regulation affects data privacy - that is, how different countries may have different demands in terms of data subject privacy; You'll learn about data governance structures, and how they can ensure that data ethics are taken care of in a centralised, standardised manner; You'll learn about defining security controls by data classification, which allows us to protect more sensitive data with stricter controls and vice-versa; You'll learn about media downgrading and/or redacting, which removes sensitive information from a medium to allow it to be shared more freely; You'll learn about data de-identification and anonymisation, consisting of removing the personal elements of data to be able to use them without exposing PII (personally identifiable information); You'll learn about the "compliance approach" to data ethics, consisting of treating ethics like a type of compliance the organisation must ensure, and how it enforces ethics; You'll learn about actually implementing ethics in an organisation, with principles, governance structures and feedback loops, allowing transparency and iteration; You'll learn about ethical data dimensions, which are just like the "vanilla" dimensions in DM, but specifically to measure ethics, such as Transparency, Fairness, Utility and others; You'll learn about data processing purposes and authority, which consists of defining specific purposes for which data may be used (or not); MY INVITATION TO YOU Remember that you always have a 30-day money-back guarantee, so there is no risk for you. Also, I suggest you make use of the free preview videos to make sure the course really is a fit. I don't want you to waste your money. If you think this course is a fit and can take your data quality knowledge to the next level... it would be a pleasure to have you as a student. See you on the other side! | https://www.udemy.com/course/data-management-and-governance/#instructor-1 | I have what could be considered an unconventional background as a coach. I don’t come from psychology or medicine. In fact, I come from tech. I created two tech startups that reached million-dollar valuations, backed by the MIT-Portugal IEI startup accelerator, afterwards becoming its Intelligence Lead. After years of coaching and mentoring startup founders on talent management, emotional management, influence and persuasion, among other topics, I started being requested by executives and investors, like venture capitalists, with more complex, large-scale problems. After years of doing executive work, I started specializing in coaching asset management professionals. With the signing of my first fund manager/CIO clients, I started adapting my performance and influence techniques for purposes such as talent management for PMs and analysts, fundraising from allocators, effective leading a team, and properly assessing talent for compensation/promotion/allocation increases. I currently provide performance coaching and influence/persuasion coaching for executives and asset management professionals, mostly but not limited to purposes like managing people, leading and closing sales/capital commitments. | Misc | Founder/Entrepreneur | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | >=1 Lakh | ||||||||||||||||
Machine Learning & Training Neural Network in MATLAB | Machine Learning | Learn concepts of Machine Learning and how to train a Neural Network in MATLAB on Iris data-set. | 2.8 | 177 | 7483 | Created by Sarthak Mishra | Apr-18 | English | $9.99 | 1h 29m total length | https://www.udemy.com/course/machine-learning-training-neural-network-in-matlab/ | Sarthak Mishra | PhD Scholar | 2.8 | 177 | 8056 | Machine Learning is the most evolving branch of Artificial Intelligence. Through this course, you will get a basic understanding of Machine Learning and Neural Networks. You will also learn to train a Neural Network in MATLAB on Iris data-set available on UCI Machine Learning repository. The data set is simple and easy to understand and also small in size. MATLAB is a multi-paradigm numerical computing environment.. | https://www.udemy.com/course/machine-learning-training-neural-network-in-matlab/#instructor-1 | I am Post Graduate in Computer Applications and currently pursuing PhD in India. I have always been passionate about Teaching and I am constantly learning new technologies and their application. I have also cleared UGC National Eligibility Test (NET) several times . I am working on few research papers as well. | Machine Learning | >=2 | Below 1K | Below 10K | >=2 | Below 1 K | Below 10 K | ||||||||||||||||||
Complete Python for data science and cloud computing | A complete & in-depth use case course taught by data science PHD & business consultants with thousand examples | 4 | 176 | 1353 | Created by Datagist INC | Sep-18 | English | $9.99 | 48h 49m total length | https://www.udemy.com/course/complete-python-for-data-science-and-cloud-computing/ | Datagist INC | teacher | 4 | 562 | 3635 | In this nearly 50 hours course, we will walk through the complete Python for starting the career in data science and cloud computing! This is so far the most comprehensive guide to mastering data science, business analytics, statistical tests & modelling, data visualization, machine learning, cloud computing, Big data analysis and real world use cases with Python. Data science career is not just a traditional IT or pure technical game – this is a comprehensive area, and above all, you must know why you conduct data analysis and how to deploy your results to generate values for the company you are working for or your own business. Therefore, this course not only covers all aspects of practical data science, but also the necessary data engineering skills and business model & knowledge you need in different industries. Whether you are working in financing, marketing, health companies, or you are running start-up, knowing the complete application of Python for data science and cloud computing is the must to achieving various business objective and looking insights into data. Yes, this complete course introduces you to a solid foundation based on the following contents and features · Python programming for data analytics, including Python fundamentals, Numpy array, Pandas Data Frames and Scipy functions. · How big data are collected and analyzed based on many real world examples. such as using Python scraping web data, communicating with flat files, parquet files, SAS data, SQLite, MongoDB and Redshift on AWS · Statistics and its application into various types of business use cases, such as the most useful statistical techniques you’ll need for banking, risk, marketing, pricing, social medium, fraud detection, customers churn & life value analysis and more. · Machine learning algorithms in each use case – all necessary theories and usages for real world applications. Note, this part is taught by both business analyst and PHD mathematician with more than 20 years experience, we teach you ‘why’ from the root, rather than just ‘model.fit() model.predict()’ instructed in many other courses. · Data visualization combined with statistical analysis use cases to help students develop a working familiarity to understand data by graph. We will teach you how to apply all famous graphics tools such as matplotlib, plotly online and offline, seaborn and ggplot into many practical cases. · Many hands-on real world projects to review and improve what you have learned in the lectures. For example, we have provided the following typical use cases along with the business backgrounds: Pricing retail products by checking elasticity; Online sales forecasting using time course data; Recommender system by transaction segmentation; Consumer credit score system; Fraud detection and performance tracking; Natural Language Processing for sentimental analysis and more. · Spark for big data analysis, cloud computing, machine learning on AWS and Azure. We provide detailed technical explanation and real word uses cases on the real cloud environments including the specific process of system configuration. · Features for listening by doing: the best way to become an expert is to practice while learning. This course is not an exception. Not only we’ll each programming codes and theories, but also need your involvement into reviewing you have learned. · Hundreds to thousands exercises, projects and homework along with detailed solutions. You can hardly find any other similar course with so many hands-on opportunities to solve so many practical problems · Our experts team will provide comprehensive online support. The course will also be on-going updated with announcement Upon completing this course, you’ll be able to apply Python to solve various data science, machine learning, statistical analysis and business problems under different environments and interfaces. You can answer different job interview questions and integrate Python and cloud computing into complete applications. Want to be successful? then join this course and follow each learning-practicing step! You’ll learn by doing and meet various challenges to become a real data scientist! | https://www.udemy.com/course/complete-python-for-data-science-and-cloud-computing/#instructor-1 | We are a group of data analytics experts involved in different industry fields -- finance, marketing, health, telecommunication and entertainment. We all have at least Master Degree of Science in computer science, mathematics and business. We all have very rich experience in education. Our goal is to educate persons who wish to learn various data analytics knowledge, skills and tools. We are always seeking innovative methods in delivering what we know. | Python | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
iOS Machine Learning with Core ML 2 and Swift 5 | Learn how to integrate machine learning into iOS apps. Hands-on Swift 5 coding using CoreML 2, Vision, NLP and CreateML. | 4.3 | 175 | 1377 | Created by Karoly Nyisztor • Professional Software Architect | Jul-22 | English | $9.99 | 2h 10m total length | https://www.udemy.com/course/machine-learning-with-core-ml-2-and-swift/ | Karoly Nyisztor • Professional Software Architect | Senior Software Engineer, Author, Inventor | 4.5 | 7925 | 34458 | A practical and concise Core ML 2 course you can complete in less than three hours. Companion eBook included! Wouldn't it be great to integrate features like synthetic vision, natural language processing, or sentiment analysis into your apps? In this course, I'll teach you how to unleash the power of machine learning using Apple Core ML 2. I'll show you how to train and deploy models for natural language and visual recognition using Create ML. I'm going to familiarize you with common machine learning tasks. We'll focus on practical applications, using hands-on Swift coding. We're going to demystify what machine learning is by investigating how it works. And no worries, I introduce each concept using simple terms, avoiding confusing jargon. We'll delve into advanced topics like synthetic vision and natural language processing. You'll apply what you've learned by building iOS applications capable of identifying faces, barcodes, text, and rectangular areas in photos in real-time. You'll learn how to train machine learning models on your computer. You're going to develop several smart apps, including a flower recognizer and an Amazon review sentiment analyzer. And there's a lot more! What qualifies me to teach you? I have more than 25 years of software development expertise. I've worked for companies like Apple, Siemens, and SAP. As a software architect, I have designed and built several enterprise systems and frameworks, including core components of Siemens Healthcare's syngo image processing system. I'm one of the senior software architects behind the SAP Cloud Platform SDK for iOS, a framework built by Apple and SAP. I currently hold twelve patents related to inventions in the field of mobile computing. Topics include: - Understanding the machine learning frameworks provided by Apple - Natural language text processing using the NaturalLanguage framework - Setting up a Core ML project in Xcode - Image analysis using Vision - Training an image classifier on your computer using CreateML - Determining the tonality of an Amazon product review "Machine Learning with CoreML 2 and Swift 5" is the perfect course for you if you're interested in machine learning. STUDENT REVIEWS “Thank you Karoly, you have delivered another excellent course, with detailed explanations and real world examples of machine learning that any app developer will be able to put into practice with their app development.Excellent course.” - Jim McMillan “This course is the best introduction to Machine Learning with Swift. It is going to familiarize you with common machine learning tasks and is very helpful for beginners.” - Zbyszek Pietras “I've been looking for a course that teaches CoreML2 with natural language processing and CreateML. I found this course very useful and gets directly to the important topics. I also appreciated the Vision CoreML section as well.” - Dan Gray MORE THAN AN ONLINE COURSE. WITH THIS CLASS, YOU ALSO RECEIVE: Personalized support As a student of this course, you’ll get access to the course's private forum, where I answer questions and provide support if necessary. The companion eBook Downloadable resources You get downloadable demo projects you can use to follow along. Continuous updates I keep enhancing this course to provide fresh and up-to-date content. OUR 30-DAY MONEY-BACK GUARANTEE If you aren't satisfied with your purchase, we'll refund you your money. We want to make sure you're completely satisfied with the course. That's why we're happy to offer you this money-back guarantee. Go ahead and click the enroll button. See you in the first lesson! | https://www.udemy.com/course/machine-learning-with-core-ml-2-and-swift/#instructor-1 | Károly Nyisztor is a professional software engineer, instructor, and author. So far, he has inspired over 150,000 students worldwide. As an instructor, he aims to share his more than 25 years of software development expertise and change the lives of students throughout the world. He's passionate about helping people reveal hidden talents and guide them into the world of startups and programming. You can find his courses and books on all major platforms, including Udemy, LinkedIn Learning, Pluralsight, Amazon Kindle, and Apple Books. Karoly has worked for companies like Apple, Siemens, and SAP. As a software architect, he has designed and built several enterprise frameworks. He currently holds twelve patents related to inventions in the field of mobile computing. He has worked with various technologies and programming languages, including x86 Assembly, C, C++, Java, Objective-C, Swift, and Python. Karoly has built several successful iOS apps and games that Apple has featured as ”New and Noteworthy,” ”App of the Month,” and “Best Travel Apps.” After 18 years, he left the corporate world to start his own business. He is the founder of LEAKKA, a software development and tech consulting company. Since 2016, he has been fully committed to teaching. Karoly teaches Software Architecture, Object-Oriented Programming and Design, Software Security, iOS Programming, Machine Learning, Swift and Python Programming, and UML. | Machine Learning | Senior Role | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
R Programming:For Data Science With Real Exercises | Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2 | 3.9 | 175 | 52639 | Created by Zulqarnain Hayat | Apr-20 | English | $9.99 | 4h 53m total length | https://www.udemy.com/course/r-language-with-hands-on-experience/ | Zulqarnain Hayat | Enterprise Database Architect | 4 | 3976 | 181891 | This course will introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions. The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages. The following topics are include: · What is R? · Installing R and R studio (IDE) · Creating bar character for categorical variables · Building histograms · Calculating frequencies and descriptive · Computing new variables · Creating scatter plots · Comparing means =================================QUICK DEMO================================== R language basics commands Reading ,Accessing and Summarizing Data in R Quick Install R language on UBUNTU Linux | https://www.udemy.com/course/r-language-with-hands-on-experience/#instructor-1 | I have done Master in Computer science (IT) and additionally I have more than 17 years of professional experience UNIX administration, Oracle database administration with different flavors (Solaris, Linux, Oracle Linux, Red Hat) and working as role Database specialist.I have completed command over following functions and skills. Key Skills: · Oracle Database Administration · MS SQL Server Administrations · MYSQL/MariaDB Administration · Sun Solaris Administration (Certified) · Linux Administration (Red Hat ,CENTOS,UBUNTU) · Oracle Application Server Administration · Banner UDC technology stack DBA track · Application Administration (LAMP,LEMP) · Oracle EBS Administration · Oracle Database Administration (RAC, Data Guard) · Oracle Business Intelligence Administration · Hyperion /ESBASE/WEB LOGIC · Windows Server Administration · Oracle Data Warehouse Builder · SQL Server Administration · Project Management · Problem solving skills and approach · Systems audit and control · Oracle OAM 11G,OSSO 10G EBS R12 · Data Center Design and Implementation · VMware Infrastructure · EMC Storage and Legato Networker Administration | Misc | Architect | >=3 | Below 1K | >=50K | >=4 | Below 10 K | >=1.5 Lakh | |||||||||||||||||
Python Crash Course for Data Science and Machine Learning | Learn the Python fundamentals from scratch and kick-off your practical data science learning path | 3.9 | 175 | 9569 | Created by Idan Gabrieli | Sep-22 | English | $9.99 | 1h 39m total length | https://www.udemy.com/course/python-crash-course-for-data-science-and-machine-learning/ | Idan Gabrieli | Online Teacher | Data, Cloud, AI | 4.5 | 6697 | 137748 | Unleash the Power of ML Machine Learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and ML experts to develop products, features, and projects that will help them unleash the power of machine learning. As a result, a data scientist is one of the top ten wanted jobs worldwide! Starting with Python This course is designed for beginners looking to enter the practical side of data science. You will learn the Python fundamentals and syntax for developing data science projects by using the JupyterLab tool while creating Jupiter notebooks. The course includes a summary exercise as well as a complete solution to practice Python. The Game just Started! Enroll in the training program and start your journey to become a data scientist! | https://www.udemy.com/course/python-crash-course-for-data-science-and-machine-learning/#instructor-1 | For the past decade, Idan Gabrieli has been working in various engineering positions at the heart of Israel's high-tech industry, also called the start-up nation. Through his career, Idan has gained extensive experience working with hundreds of business companies, helping them transform challenges and opportunities into practical use cases while leveraging cutting-edge technologies. Idan has comprehensive knowledge that spans multiple domains, including cloud computing, machine learning, data science, electronics, and more. In 2014, Idan started to create and publish online courses on various topics while teaching thousands of students worldwide. In 2021-2022, Idan was recognized as a top-seller and high-rated instructor in multiple leading educational providers. As part of his teaching style, Idan is well-known for simplifying complex technology topics and providing high-quality educational content suitable to the relevant audience. Every course has specific learning objectives, easy-to-follow structure, and straight-to-point material while combining various multimedia teaching options. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | Below 10K | >=4 | Below 10 K | >=1 Lakh | |||||||||||||||||
Artificial Intelligence and Predictive Analysis | This course is a comprehensive understanding of AI concepts and its application using Python and iPython. | 4 | 175 | 62483 | Created by EDUCBA Bridging the Gap | Dec-18 | English | $9.99 | 6h 21m total length | https://www.udemy.com/course/artificial-intelligence-with-python/ | EDUCBA Bridging the Gap | Learn real world skills online | 4 | 4791 | 252401 | Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This course is a comprehensive understanding of AI concepts and its application using Python and iPython. The training will include the following; What is Artificial Intelligence? Intelligence Applications of AI Problem solving AI search algorithms Informed (Heuristic) Search Strategies Local Search Algorithms Learning System Common Sense Genetic algorithms Expert Systems Scikit-learn module What is Artificial Intelligence? The first idea of artificial intelligence was given by scientist Mr. Alan Turing around the time of the second world war. He suggested building a machine that can mimic the understanding of human intelligence and act like a human. Artificial Intelligence today is used in all fields of work specifically banking, insurance, manufacturing, retail, logistics and so on. Its application in medical diagnosis, robots, remote sensing, etc. is a high state of the art. AI as a subject includes the use of computer science, mathematics, statistics and domain expertise. AI has great advantages and so of them are mentioned below: It provides greater precision and accuracy on detection and prediction Robots trained on AI can be used to do the works which are difficult for us AI has created newer technological breakthroughs in our life Fraudulent activities such as credit card transactions have become easier with AI technologies AI can be used in time-consuming tasks and it can save a lot of time by becoming more efficient. You will be able to build the following as a practical project: – Classifiers of various types Logic Programming based optimizers Heuristic Search performed on NP-complete problems Natural Language Processing on text data Machine Learning in general for several kinds of data Logic and reasoning for model evaluation and interpretation Rule-based Programming for business use cases Decision Making based on AI and ML Stochastic methods such as time series and HMM | https://www.udemy.com/course/artificial-intelligence-with-python/#instructor-1 | EDUCBA is a leading global provider of skill based education addressing the needs of 1,000,000+ members across 70+ Countries. Our unique step-by-step, online learning model along with amazing 5000+ courses and 500+ Learning Paths prepared by top-notch professionals from the Industry help participants achieve their goals successfully. All our training programs are Job oriented skill based programs demanded by the Industry. At EDUCBA, it is a matter of pride for us to make job oriented hands-on courses available to anyone, any time and anywhere. Therefore we ensure that you can enroll 24 hours a day, seven days a week, 365 days a year. Learn at a time and place, and pace that is of your choice. Plan your study to suit your convenience and schedule. | Artificial Intelligence | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2.5 Lakh | ||||||||||||||||||
Modern Data Analysis Masterclass in Pandas and Python | Build your Data Analysis and Visualization skills with Python’s Pandas, Numpy, Matplotlib and Seaborn Libraries | 4.7 | 175 | 1909 | Created by Dr. Ryan Ahmed, Ph.D., MBA, Ligency Team, Mitchell Bouchard, Ligency I Team | Jun-21 | English | $9.99 | 20h 48m total length | https://www.udemy.com/course/modern-data-analytics-masterclass/ | Dr. Ryan Ahmed, Ph.D., MBA | Professor & Best-selling Instructor, 300K+ students | 4.5 | 30665 | 313620 | The data revolution is here! Data is the new gold of the 21st Century. Companies nowadays have access to a massive amount of data and their competitive advantage lies in their ability to gain valuable insights from this data. Not only do they need to analyze all the data, but they need to do it fast! Data can empower companies to boost their revenues, improve processes and reduce costs. Data could be leveraged in many industries such as Finance, banking, healthcare, transportation, and technology sectors. The purpose of this course is to provide you with knowledge of key aspects of data analytics in a practical, easy, and fun way. The courseprovides students with practical hands-on experience using real-world datasets. We will learn how to analyze data using Pandas Series and DataFrames, how to perform merging, concatenation and joining. We will also learn how to perform data visualization using Matplotlib and Seaborn. Furthermore, we will learn how to deal with datetime and text dataset. So, whether you're just getting started with Python and Data Analysis, or you're well-established in your career and would like to polish your data visualization skills, this course will boost your skillset. So, are you ready to get your data visualizations up and running? Enroll now! | https://www.udemy.com/course/modern-data-analytics-masterclass/#instructor-1 | Dr. Ryan Ahmed is a professor and best-selling online instructor who is passionate about education and technology. Ryan has extensive experience in both Technology and Finance. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University with focus on Mechatronics and Electric Vehicles. He also received a Master of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. He has taught 46+ courses on Science, Technology, Engineering and Mathematics to over 300,000+ students from 160 countries with 11,000+ 5 stars reviews and overall rating of 4.5/5. Ryan also leads a YouTube Channel titled “Professor Ryan” (~1M views & 22,000+ subscribers) that teaches people about Artificial Intelligence, Machine Learning, and Data Science. Ryan has over 33 published journal and conference research papers on artificial intelligence, machine learning, state estimation, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA. * McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world. | Python | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=3 Lakh | |||||||||||||||||
Machine Learning 2022: Complete Maths for Machine Learning | Learn Math for Machine Learning, Math for Data Science, Linear Algebra, Calculus, Vectors & Matrices, Probability & more | 3.9 | 174 | 901 | Created by Jitesh Khurkhuriya, Python, Data Science & Machine Learning A-Z Team | Apr-22 | English | $10.99 | 2h 26m total length | https://www.udemy.com/course/machine-learning-2020-complete-maths-for-machine-learning/ | Jitesh Khurkhuriya | Data Scientist and Digital Transformation Consultant | 4.6 | 9128 | 54236 | Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Mathematics form the basis of almost all the Machine Learning algorithms. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it. You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you can not correlate them with Machine Learning. This course of Math For Machine Learning, aims to bridge that gap. We will get you upto speed in the mathematics required for Machine Learning and Data Science. We will go through all the relevant concepts in great detail, derive various formulas and equations intuitively. This course is divided into following sections, Algebra Foundations In this section, we will lay the very foundation of Algebraic Equations including Linear Equations and how to plot them. We will understand what are Exponents, Logs, Polynomial and quadratic equations. Almost all the Machine Learning algorithms use various functions for loss measurement or optimization. We will go through the basics of functions, how to represent them and what are continuous and non-continuous functions. Calculus It is said that without calculus and differential equations, Machine Learning would have never been possible. The Gradient Descent using derivatives is essence of minimizing errors for a Machine Learning algorithm. We will understand various terms of Rate of Change, Limits, What is Derivative, including Single, Double and Partial Derivatives. I will also explain with an example, how machine learning algorithms use calculus for optimization. Linear Algebra Linear Algebra is the mathematics of the 21st Century. Every record of data is bound by some form of algebraic equation. However, it's nearly impossible for humans to create such an equation from a dataset of thousands of records. That's where the ability of vectors and matrices to crunch those numerical equations and create meaningful insights in the form of linear equations help us. We will see, right from the foundations of Vectors, Vector Arithmetic, Matrices and various arithmetic operations on them. We will also see, how the vectors and matrices together can be used for various data transformations in Machine Learning and Data Science. Probability Probability plays an important role during classification type of machine learning problems. It is also the most important technique to understand the statistical distribution of the data. Conditional probability also helps in classification of the dependent variable or prediction of a class. With all of that covered, you will start getting every mathematical term that is taught in any of the machine learning and data science class. Mathematics has been my favorite subject since the childhood and you will see my passion in teaching maths as you go through the course. I firmly believe in what Einstein said, "If you can not explain it simple enough, You have not understood it enough.". I hope I can live upto this statement. I am super excited to see you inside the class. So hit the ENROLL button and I will see you inside the course. You will truly enjoy Mathematics For Machine Learning.... | https://www.udemy.com/course/machine-learning-2020-complete-maths-for-machine-learning/#instructor-1 | Jitesh has over 20 years of technology experience and worked as programmer, Product Head as well as the Data Scientist. Jitesh has worked with various fortune 500 companies and governments across the world. As the Data Scientist and Anti-Fraud Expert, he was the member of the high-profile team to suggest tax reforms and amendments in VAT, Customs and Income Tax based on fraud pattern analysis, countrywide data mining and analysis, business process security analysis. This not only contributed to a revolutionary change in the tax processes but also reduced the tax and customs frauds. As a seasoned leader in Digital Transformation, Jitesh has developed and executed strategies that generated high top and bottom line revenue streams. | Machine Learning | Consultant | Yes | >=3 | Below 1K | Below 1K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Artificial Intelligence Introduction for Non-Technicals | Introduction to AI, ML, Data Science , BI and Analytics for Non-Technicals, Leaders, Managers, freshers and Beginners | 4.1 | 173 | 523 | Created by Sudhanshu Saxena | Jul-20 | English | $9.99 | 3h 18m total length | https://www.udemy.com/course/artificial-intelligence-for-everyone/ | Sudhanshu Saxena | Data Scientist, Machine Learning & Big Data Consultant | 4.5 | 207 | 6866 | Section 1-L1: To learn the strategy of various skills of current and future world like Artificial Intelligence, Machine learning, Data Science, we are starting from understanding data. To expertise in Artificial Intelligence needs to be understood the basics of data. In this INTRODUCTION section, we will talk about What is the data? How does data divide into multiple parts? Types of data! How do and where the data generate from? What kind of data available globally? How we can deal with the data? Apart from that, we will discuss the Characteristics of the structured data. Sources of the Structured data. Section 1-L2: The questions you should seek are, How Machine Learning, Artificial Intelligence can handle this. As we understood the Data, its type, and the structured data, here we will talk about the Unstructured Data. This second lecture will be covering Types of Unstructured data. Advantages and disadvantages of unstructured data. Problem faced in storing unstructured Data Section 1-L3: Out of all available data, the most crucial data is Semi-structured Data which allows the user to have a flexible Schema. In the previous lecture, we talked about what kinds of data can be deal with, the type of data, its advantages--disadvantages of unstructured data. Here we will be learning about the most useful type of data called -Semi-Structured Data. Characteristics of Semi-structured Data Source of Semi-structured Data. Advantages and disadvantages of Semi-structured data. Section 2-L4: In the previous section, we understood the Data, its type, Advantages-Disadvantages of different kinds of data and where data comes from. So here, in this section, we will cover: - What is Big Data? Why even we care about it? What can be done with this Bigdata? The Hype around Big Data? Section 2-L5: This lecture is intended to cover the term Bigdata-Why any data called Bigdata? How to identify if my data is Bigdata? What are the properties of the Bigdata? Do I see the similar properties in my Data also? What are the characteristics of Bigdata? How do you store the Bigdata? Where to store Bigdata? What tools and tricks are used to handle Bigdata? Four dimensions of Bigdata? Section 2-L6: if we understand the data its size and types of data, we should know who is creating this data. This section is covering all aspects of Bigdata including: How much bigdata I am (an individual) accumulating? How tough it is for us to deal with this kind of data? What are the challenges in handling this kind of Bigdata? Section 2-L7: we will also cover the Model of BigData generation? This lecture describes how this Big-data gets generated Why this data is huge now? What do organizations want? Which all companies are working on Bigdata? Get answers to all these questions in this video. Section 3-L8: This new section is intended to describe what is Analytics and why it came into existence and Understanding Analytics from scratch. To understand that we will seek the answers to all possible questions like What are the four major questions we want to answer? What kind of analytics are possible? What is the difference among all this Analytics-Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics? Section 4-L9 As data is the basic foundation of AI, with its existence, types analysis and analytics Business intelligence plays an important role in using huge sized data. To understand BI we will cover How to answer the first basic question of analytics? How to get started with analytics? What all organizations can achieve with Business Intelligence? What are the Functions of Business Intelligence? How to implement a Business Intelligence system in an organization? What kind of data Business Intelligence require? Section 5- L10 Here you are ready to learn why and what Artificial Intelligence is? we will start from Understanding Artificial Intelligence from scratch covering all questions like What are the different definitions of Artificial Intelligence? Why it is needed to create Artificial Intelligence? What are the types of AI? How to see through Artificial Intelligence? Applications of Artificial Intelligence. Section 6-L11: Machine learning is a part of AI and Data Science. It is necessary to understand ML if you are dealing with AI. here we will be Understanding Machine Learning from scratch. Different definitions of Machine Learning. Types of Machine Learning How each type of ML is different from another and where are they going to use? How does the computer understand the data? Section 6-L12 Where we can use and see ML applications in our Daily Life How to use Machine Learning in real-time applications.? ML use case in similar Pins ML use case in face recognition. ML use case in people you may know ML use case in spam Email filtering ML use case in Product recommendations ML use case in online fraud detections ML use case in Disease identifications ML use case in Personalised treatment ML use case in clinical trial research ML use case in character recognitions Section 7: L13 This section is all about AI from very scratch, we discuss all sections of AI, ML and now we talk about Data Science, How does data science relate to Artificial Intelligence? To answer this question, we will discuss What is Data Science? The definition of Data Science? How does Data Science connect with analysis? Components of Data Science. Data Science Lifecycle. Use of Mathematics in Data Science? Use of Machine Learning in Data Science. Difference between Business Intelligence and Data Science. | https://www.udemy.com/course/artificial-intelligence-for-everyone/#instructor-1 | Data Scientist | Machine learning Expert | Big Data Evangelist | Statistics | Predictive Analysis| Artificial Intelligence and Big Data Training. Data Science and consultant with more then 12+ years of experience. 3000+ hours of classroom and online training. Visiting Faculty for Big Data Hadoop for many institutes pan India level Big Data and Artificial Intelligence speaker. | Artificial Intelligence | Consultant | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Data Science A-Z : Machine Learning with Python & R | By Data Scientist / IITian for Beginners . Data Science/Machine Learning with Python & R for beginners to advance | 4 | 173 | 678 | Created by Arpan Gupta | Mar-20 | English | $9.99 | 12h 18m total length | https://www.udemy.com/course/machine-learning-using-r/ | Arpan Gupta | Data Scientist / IITian | 4.1 | 223 | 920 | Interested in the field of Data Science & Machine Learning? Then this course is for you! Learn Data Science & Machine Learning by doing! Hands On Experience Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This course is for those : Anyone interested in Machine Learning. Students who have at least high school knowledge in math and who want to start learning Machine Learning. Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools. What is Data Science ? Data science is used to extract patterns or insights from data to predict future or to understand customer behavior and so on. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data Mining large amounts of structured and unstructured data to identify patterns can help an organization to reduce costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage. Some Data Science and machine learning Applications Netflix uses data science & machine learning to mine movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce. Companies like Flipkart and Amazon uses data science and machine learning to understand the customer shopping behavior to do better recommendations. Gmail's spam filter uses data science (machine learning algorithm) to process incoming mail and determines if a message is junk or not.. Proctor & Gamble utilizes data science (machine learning ) models to more clearly understand future demand, which help plan for production levels more optimally. Why Programming Won't Work in some Cases?? Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. 1. For automatic washing machine,we can write programs for the washing machine functionality. 2. For automated cars without drivers in high traffic.Just imagine ,how complex and dangerous it will be when someone starts coding /programming for such functionalities.For cars to automate we would require something which is called "Machine Learning " COURSE DETAILS AS BELOW : DATA STRUCTURES ,etc. in R & PYTHON as follows : 1. Vectors 2. Matrices 3. Data Frames 4. Factors 5. Numerical/Categorical Variables 6. List 7. How to convert matrix into data frame PROGRAMMING IN R &PYTHON DATA VISUALIZATION IMPLEMENTATION OF MACHINE LEARNING MODELS as follows: 1. Linear Regression & Logistic Regression 2. Decision Tree 3. Random Forest 4.Neural Networks 5. Deep learning 6. H2o framework 7. Cross validation /How to avoid Over fitting 8. Dimensionality Reduction Techniques LEARN FROM SCRATCH [HOW TO DO ML IN PYTHON] SEE IN REAL TIME HOW OPTIMIZATION WORKS TO GET A MACHINE LEARNING MODEL All the materials for this data science & machine learning course are FREE. You can download and install R & Python, with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use".It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. THE COURSE IS DESIGNED IN SUCH A WAY WHICH GIVES MORE OF PRACTICAL SENSE FOR MACHINE LEARNING & DATA SCIENCE IN VERY LESS AMOUNT OF TIME So what are you waiting for ? Enroll in this course and start your future journey !! | https://www.udemy.com/course/machine-learning-using-r/#instructor-1 | I'm fond of Research in general and Machine learning specifically.My education background is Applied Physics with specialization on nano semiconductors.I have done my Masters in Applied Physics from Indian Institute of Technology,Roorkee. I do love teaching in general and my passion is to learn new things.I'm working as a data scientist currently. | Machine Learning | Data Scientist | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 1 K | |||||||||||||||||
Machine Learning and Statistical Modeling with R Examples | Learn how to use machine learning algorithms and statistical modeling for clustering, decision trees, etc by using R | 4.2 | 172 | 2274 | Created by R-Tutorials Training | Sep-16 | English | $9.99 | 2h 22m total length | https://www.udemy.com/course/machine-learning-and-statistical-modeling-with-r/ | R-Tutorials Training | Data Science Education | 4.5 | 32278 | 263444 | See things in your data that no one else can see – and make the right decisions! Due to modern technology and the internet, the amount of available data grows substantially from day to day. Successful companies know that. And they also know that seeing the patterns in the data gives them an edge on increasingly competitive markets. Proper understanding and training in Machine Learning and Statistical Modeling will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can boost your career! Marketing companies use Machine Learning to identify potential customers and how to best present products. Scientists use Machine Learning to capture new insights in nearly any given field ranging from psychology to physics and computer sciences. IT companies use Machine Learning to create new search tools or cutting edge mobile apps. Insurance companies, banks and investment funds use Machine Learning to make the right financial decisions or even use it for algorithmic trading. Consulting companies use Machine Learning to help their customers on decision making. Artificial intelligence would not be possible without those modeling tools. Basically we already live in a world that is heavily influenced by Machine Learning algorithms. 1. But what exactly is Machine Learning? Machine learning is a collection of modern statistical methods for various applications. Those methods have one thing in common: they try to create a model based on underlying (training) data to predict outcomes on new data you feed into the model. A test dataset is used to see how accurate the model works. Basically Machine learning is the same as Statistical Modeling. 2. Is it hard to understand and learn those methods? Unfortunately the learning materials about Machine Learning tend to be quite technical and need tons of prior knowledge to be understood. With this course it is my main goal to make understanding those tools as intuitive and simple as possible. While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with data on a regular basis can benefit from this course. 3. How is the course structured? For a better learning success, each section has a theory part, a practice part where I will show you an example in R and at last every section is enforced with exercises. You can download the code pdf of every section to try the presented code on your own. 4. So how do I prepare best to benefit from that course? It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (courses R Basics and R Level 1). You should also know the basics of modeling and statistics and how to implement that in R (Statistics in R course). For special offers and combinations just check out the r-tutorials webpage which you can find below the instructor profile. What R you waiting for? Martin | https://www.udemy.com/course/machine-learning-and-statistical-modeling-with-r/#instructor-1 | R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available. All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. We are here to help you boost your career with analytics training – Just learn and enjoy. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | ||||||||||||||||||
Question Generation using Natural Language processing | Auto generate assessments in edtech like MCQs, True/False, Fill-in-the-blanks etc using state-of-the-art NLP techniques | 4.5 | 172 | 629 | Created by Ramsri Golla | Mar-22 | English | $34.99 | 5h 30m total length | https://www.udemy.com/course/question-generation-using-natural-language-processing/ | Ramsri Golla | Lead Data Scientist (NLP & CV) | 4.4 | 176 | 641 | This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc. All the sections will be accompanied by easy to use Google Colab notebooks. You can run Google Colab notebooks for free on the cloud and also train models using free GPUs provided by Google. Prerequisites: This course will focus on the practical use cases of algorithms. A high-level introduction to the algorithms used will be introduced but the focus is not on the mathematics behind the algorithms. A high-level understanding of deep learning concepts like forward pass, backpropagation, optimizers, loss functions is expected. Strong Python programming skills with basic knowledge of Natural Language processing and Pytorch is assumed. The course outline : ➤ Generate distractors (wrong choices) for MCQ options Students will use several approaches like Wordnet, ConceptNet, and Sense2vec to generate distractors for MCQ options. ➤ Generate True or False questions using pre-trained models like sentence BERT, constituency parser, and OpenAI GPT-2 Students will learn to use constituency parser from AllenNLP to split any sentence. They will learn to use GPT-2 to generate sentences with alternate endings and filter them with Sentence BERT. ➤ Generate MCQs from any content by training a T5 transformer model using the HuggingFace library. Students will understand the T5 transformer algorithm and use SQUAD dataset to train a question generation model using HuggingFace Transformers library and Pytorch Lightning. ➤ Generate Fill in the blanks questions Students will learn to use Python Keyword extraction library to extract keywords, use flashtext library to do fast keyword matching, and visualize fill-in-the-blanks using HTML ElementTree in Colab ➤ Generate Match the following questions. Students will learn to use Python Keyword extraction library to extract keywords, use flashtext library to do fast keyword matching, and use BERT to do word sense disambiguation (WSD). ➤ Deploy question generation models to production. Deploy transformer models like T5 to production in a serverless fashion by converting them to ONNX format and performing quantization. Create lightweight docker containers using FastAPI for transformer model and deploy on Google Cloud Run. | https://www.udemy.com/course/question-generation-using-natural-language-processing/#instructor-1 | Ramsri is a lead data scientist with 8+ years of work experience at startups and large corporations across Silicon Valley, Singapore, and India. Most recently he has been a co-founder and CTO of an AI-assisted assessments startup. Ramsri is very keen on mapping cutting edge NLP and computer vision research to practical real-world use. | NLP | Chief/Lead Role | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 1 K | |||||||||||||||||
Introduction to Artificial Intelligence: AI for beginners | Introduction to Artificial Intelligence, basics' Course for beginners & everyone. Fundmantals for AI&ml, neural networks | 4 | 171 | 6396 | Created by Nikoloz Sanakoevi | Oct-19 | English | $9.99 | 1h 20m total length | https://www.udemy.com/course/introduction-to-artificial-inteligence/ | Nikoloz Sanakoevi | Web Development and Programming Instructor | 3.7 | 1334 | 65368 | Hi and welcome to the introduction to artificial intelligence course. In this course we will talk about the past, present and the future of AI. This course covers all the introductory topics to AI to get you started on the path of becoming AI specialist. You will learn about main philosophy, history and approaches of AI as well as its applications. Learn the Fundamental Consents of Artificial Intelligence and become ready to master the Field of AI Learn about the history and founding fathers of AI Learn about 4 types of AI and 3 main domains of AI technology Get familiar with the main fields of AI research and applications of artificial intelligence Learn about the basics of Neural Networks, Fuzzy Logic and Genetic Algorithms Learn the basics of Case-Based Reasoning, Bayesian networks and Behavior Based approaches Know the main advantages and disadvantages associated with artificial intelligence Learn about the future possibilities and tangible projects with artificial intelligence Learn the fundamentals of AI In this course we will talk about all that you need to know to get started in the field of AI. You will get familiar with the main approaches and research fields of artificial intelligence. You will know the advantages and disadvantages of AI as well as its possible applications in the future. The course is split into 5 main sections starting from the history of AI. In this section we cover the basics and the history, next we will go into the present day applications of AI followed by the topics on the main categories and methods of AI. Lastly we will speak about cons and pros as well as the future of AI technology. | https://www.udemy.com/course/introduction-to-artificial-inteligence/#instructor-1 | Hi, my name is Nick and I will be teaching you through Udemy! I have many years of experience in programming and technical subjects. That's exactly, what I want to teach you. I am grateful for this opportunity to teach thousand of students just like you through my courses! My main focus is programming languages, but I teach other topics such as math and electronics as well. Look through my courses, maybe, you'll find something for you. | Artificial Intelligence | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Mastering OCR using Deep Learning and OpenCV-Python | A complete guide to optical character recognition pipeline using Deep Learning, python and OpenCV | 4.1 | 170 | 797 | Created by Pankaj Kang, Atul Krishna Singh | Jun-21 | English | $9.99 | 2h 35m total length | https://www.udemy.com/course/mastering-ocr-using-deep-learning-and-opencv-python/ | Pankaj Kang | Computer Vision Specialist | Blogger | Freelancer | 4.1 | 170 | 797 | Hi There! Welcome to the course 'Mastering OCR using Deep Learning and OpenCV-Python'. This is the first course of my OCR series. In this course we will start from the very basics. We will first discuss what is Optical Character Recognition and why you should invest your time in learning this. Then we will move to the general pipeline used by most of the OCR systems available. After this we will start learning each pipeline component in detail. We will start by learning some image pre-processing techniques commonly used in OCR systems. Then we will learn some deep learning based text detection algorithms such as EAST and CTPN. We will also implement the EAST algorithm using OpenCV-Python. Next we will learn the crux of the CTC which is widely used in developing text recognition systems. We will implement very famous text recognition algorithm that is CRNN. Finally we will learn the last component of the OCR pipeline that is restructuring. In this we will discuss why is restructuring important for any OCR systems. We will also discuss an open source end-to-end OCR engine which is pytesseract. Finally we will run the complete OCR pipeline to extract the data from identification document using pytesseract. So that's all for this course, see you soon in the class room. Happy learning and have a great time. Stay safe, stay healthy. | https://www.udemy.com/course/mastering-ocr-using-deep-learning-and-opencv-python/#instructor-1 | I have more than four years of experience in computer vision mainly optical character recognition. It includes designing and implementing solutions for various financial services. I also write my own blogs at TheAILearner which focuses on image processing, deep learning and explaining research papers using code. I am also building my own OCR engine app. I have done bachelors and masters from Indian institute of technology. I have also served as mentor and alpha tester at Coursera. Currently I am working as solution architect at my current organization. | Deep Learning | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 1 K | ||||||||||||||||||
Machine Learning Using Google Colab & AutoML (Google Vertex) | AutoML Google Vertex | Python | Supervised & Unsupervised Algorithms | Exploratory Data Analysis (EDA) | 4.1 | 170 | 16447 | Created by SeaportAi . | Oct-22 | English | $9.99 | 7h 1m total length | https://www.udemy.com/course/machine-learning-on-google-cloud/ | SeaportAi . | Artificial Intelligence and Business Transformation Experts | 4.1 | 2302 | 101399 | Recent Updates July 2022: AI ML industry is constantly evolving and a recent trend is the maturing of AutoML approaches. AutoML is the automation of machine learning (deploying ML models without writing any code). As practitioners, you must be conversant with this, in addition to deploying ML models using programming. We will use Google Vertex for AutoML. June 2022: A 90-page eBook on "Python Programming" has been added to this course. The eBook in pdf version can be downloaded (at no additional cost) from "Getting Started with python" video lecture. May 2022: Conditional scatter plots have been added after simple linear regression lecture. This is a very useful addition to regression. Feb 2022: Of Late, EDA Libraries (Klib & Sweetviz) do all the different types of tasks performed under EDA with a few lines of code. ---------------------------------------------------------------------------------------------------------------------- Course Description Machine learning is a subset of artificial intelligence that is at the forefront of digital transformation in the world. Thanks to machine learning, it is now possible to detect diseases, know the defaulters of a loan and know the future sales of a product. All these information can be had proactively and not as an after the fact scenario. Machine learning and artificial intelligence-based roles are in great demand in the job market and such roles offer a higher salary than traditional programming roles. This course covers the concepts of machine learning as well as the application of these concepts using case studies and examples, along with a walk through of the python codes. Python programming is also covered for the benefit of those who are new to python and those who want to refresh some of the topics in python. The following algorithms are covered in detail: Simple and multiple linear regression Logistic regression Decision tree, Random forest and XG boost Unsupervised algorithms - Cluster (kNN based) and Hierarchical. Learners will also understand how to develop the above machine learning in a cloud environment. They will learn not just to code in cloud but also to access the data stored in cloud. This will be particularly helpful to learners since many organizations are adopting cloud at a fast pace. A key aspect of the course is the coverage of Exploratory Data Analysis (EDA). EDA covers the set of activities that you do before you start the ML project. Lastly, how to pursue a machine learning project has been covered. This course is taught by an industry veteran, who brings his vast experiences and practical perspectives into the program. | https://www.udemy.com/course/machine-learning-on-google-cloud/#instructor-1 | Profile of Trainer Govind Kumar Summary Over 2 decades of experience managing Technology, Operations and Quality in top MNCs & startups. Held leadership roles (including Founder & CEO of an AI & Automation Startup) and managed businesses across Asia Pacific & Japan region. Expertise AI, Six Sigma and Innovation Key Experiences Successfully incubated Centers of Excellence for fraud prevention and service analytics. Significant experience in design thinking based product development and management. Played a critical role in developing products for emerging markets. Education & Certification B. Tech and Full time MBA from top institutes in India Certifications in six sigma and project management. Accolades Won global awards in the areas of Customer Experience, Leadership Excellence, Quality and Technology. Featured in the cover of CIO Review Magazine. Board of Studies Member of the Board of Studies at Loyola College, Chennai, India (a 96 year old institution) | Machine Learning | >=4 | Below 1K | >=15K | >=4 | Below 10 K | >=1 Lakh | ||||||||||||||||||
The Complete Machine Learning 2020|Python, Math|Dummy To Pro | Start Machine Learning & Data Science era with Python ,Math & Libraries like: SKlearn , Pandas , NumPy, Matplotlib & Gym | 4.6 | 169 | 1378 | Created by SkyHub Academy, Ahmed Attia | Nov-19 | English | $9.99 | 24h 53m total length | https://www.udemy.com/course/complete-machine-learning/ | SkyHub Academy | Learn with Passion Learn with Fun | 4.6 | 169 | 1378 | Humans learn from past experience, so why not machine learn as well? Hello there, If the word 'Machine Learning' baffles your mind and you want to master it, then this Machine Learning course is for you. If you want to start your career in Machine Learning and make money from it, then this Machine Learning course is for you. If you want to learn how to manipulate things by learning the Math beforehand and then write a code with python, then this Machine Learning course is for you. If you get bored of the word 'this Machine Learning course is for you', then this Machine Learning course is for you. Well, machine learning is becoming a widely-used word on everybody's tongue, and this is reasonable as data is everywhere, and it needs something to get use of it and unleash its hidden secrets, and since humans' mental skills cannot withstand that amount of data, it comes the need to learn machines to do that for us. So we introduce to you the complete ML course that you need in order to get your hand on Machine Learning and Data Science, and you'll not have to go to other resources, as this ML course collects most of the knowledge that you'll need in your journey. We believe that the brain loves to keep the information that it finds funny and applicable, and that's what we're doing here in SkyHub Academy, we give you years of experience from our instructors that have been gathered in just one an interesting dose. Our course is structured as follows: An intuition of the algorithm and its applications. The mathematics that lies under the hood. Coding with python from scratch. Assignments to get your hand dirty with machine learning. Learn more about different Python Data science libraries like Pandas, NumPy & Matplotlib. Learn more about different Python Machine learning libraries like SK-Learn & Gym. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the following: Simple Linear Regression Multiple Linear Regression Polynomial Regression Lasso Regression Ridge Regression Logistic Regression K-Nearest Neighbors (K-NN) Support Vector Machines (SVM) Kernel SVM Naive Bayes Decision Tree Classification Random Forest Classification Evaluating Models' Performance Hierarchical Clustering K-Means Clustering Principle Component Analysis (PCA) Pandas (Python Library for Handling Data) Matplotlib (Python Library for Visualizing Data) Note: this course is continuously updated ! So new algorithms and assignments are added in order to cope with the different problems from the outside world and to give you a huge arsenal of algorithms to deal with. Without any other expenses. And as a bonus, this course includes Python code templates which you can download and use on your own projects. | https://www.udemy.com/course/complete-machine-learning/#instructor-1 | Hi there, We're more than glad that you're reading this. We're SkyHub Academy, an educational academy specialized for the field of Machine Learning and Data Science. We've have a vision that we want everyone who have a passion inside towards that field, to easily get to the road quickly without wasting his time in meaningless classes and books that keep him away from progress. We're presenting Complete Courses that make you get your hands dirty in the fields of Data Science without the need for other resources, all in one place. Professionally, we've a group of skilled data scientists with years of experience, and they're ready to give you the help you need to achieve the mastery in the world of Data Science and Machine Learning. We're extremely excited to see you here with us in our journeys! See you there our friend. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | ||||||||||||||||||
Learning Path: R: Complete Machine Learning & Deep Learning | Unleash the true potential of R to unlock the hidden layers of data | 4.4 | 168 | 1484 | Created by Packt Publishing | Jun-17 | English | $9.99 | 17h 36m total length | https://www.udemy.com/course/learning-path-r-complete-machine-learning-deep-learning/ | Packt Publishing | Tech Knowledge in Motion | 3.9 | 68744 | 404808 | Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios. The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering. By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects. Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: About the Authors Selva Prabhakaran is a data scientist with a large e-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing Big Data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building Big Data platforms for business intelligence and customer relationship management systems. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process Big Data and apply data mining techniques for data analysis. Vincenzo Lomonaco is a deep learning PhD student at the University of Bologna and founder of ContinuousAI, an open source project aiming to connect people and reorganize resources in the context of continuous learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses machine learning and computer architectures in the same department. | https://www.udemy.com/course/learning-path-r-complete-machine-learning-deep-learning/#instructor-1 | Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development. Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge. We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them. | Machine Learning | >=4 | Below 1K | Below 10K | >=3 | Below 1 Lakh | >=4 Lakh | ||||||||||||||||||
Build Your Own Chatbot in Python | Play with your own AI Assistant | 2.4 | 167 | 16776 | Created by Kumar Rajmani Bapat | Apr-21 | English | $9.99 | 1h 59m total length | https://www.udemy.com/course/build-your-own-chatbot-in-python/ | Kumar Rajmani Bapat | Google Certified Python Expert | Google Chrome Extension Dev | 3.6 | 806 | 62382 | A faster way to learn artificial intelligence (AI) is to build real life Projects which you are going to do in this course , also will go through history of AI, and its evolution over the years. You will also learn the basics of machine learning (ML) and how it aids the rapid improvement of AI. You will also build your own chatbot, named Jarvis, and train it with secret answers once it passes all the security checks. Let's get started! This course requires fundamental knowledge of variables, loops, control statements, and lists in Python. Hands-on coding get runnable code which you can directly run and implement . Learn at your own pace and get ready by building AI based Projects. Understand the working of your Model. Will go Through Each and Every Concept to Make you Understand working of this AI Bot. You will also get Books and Resources to learn basics of Python to make you understand better. updates Codes : Up to Date Contents : Latest Materials :Exclusive Material Type : Downloadable Stay tuned I will continue bringing more such courses to stay productive and updated with latest trends in Computer Science Engineering and Data Science. Also please provide your Wonderful Feedback and Rating. Thank you ... In this hard times also continue to upgrade yourself and be trained for the future Stay safe and stay home. | https://www.udemy.com/course/build-your-own-chatbot-in-python/#instructor-1 | Hello Everyone !! I am a self-driven and motivated computer scientist with the ability to develop and maintain robust software products, web apps, and data science projects of almost any type. Having a specialization in the area of data science and software development with a core interest in AI and Cloud Technologies. I have hands-on experience in analyzing data using SQL and Python and Tableau, and Excel, also in applying machine learning algorithms to building high-accuracy models for prediction and developing large-scale software scalable applications, etc. I earlier worked as a Data Engineer Intern at Indian Meteorological Department for a year and where I worked on a live project and developed an AI-based Crop disease detection System with 92 percent accuracy. Furthermore, my major project was a B2B Ecommerce Website that was developed in Django, Flask and Java, SQLite, and Javascript. I also presented it at an International Conference and it was published in an international journal. I have completed training in the Google Cloud Platform by Google on Cloud Engineering and Data Science and Machine Learning Tracks and have good experience and understanding working with it also. Programming Languages Expertise: Java, Python, C, C++, Assembly. Global Certificates and Batches: Google Cloud Engineering and Machine Learning all tracks completed Batches, Microsoft Certifed Azure and Data Fundamental, DET. Development tools: Django, Flask, Excel, Tableau, PostgreSQL Jupyter, Visual Studio Code, GCP, Hadoop, Spark, Windows/Linux, Office, and Python Libraries for Advance data analysis. Apart from this, I am an Instructor of some amazing courses here at Udemy such as Python for Data Science and Machine learning and Flask and Now coming the latest one etc. I have also authored a few research-based books such as Predictive Data Analysis using python etc. In my spare time, I like Playing Chess, Reading Articles, Coding, Solving Statistics, and data-related problems, and Learning new things. I have also won around 20+ trophies in Open Chess Tournaments till now. I hope you like My Courses' Content and Exclusive Materials and Documentation feel free to provide your Good Rating and Feedback. And Stay Tuned and will continue bringing more such courses to stay productive and updated with the latest trends in Data Science and Machine Learning and Computer Science Engineering. If you want to contact me regarding anything you can reach me through LinkedIn or email or Messages section etc. Thank you. | Python | >=2 | Below 1K | >=15K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
100+ Exercises - Python - Data Science - NumPy - 2022 | Improve your Python programming and data science skills and solve over 100 exercises in NumPy! | 4.7 | 166 | 52550 | Created by Paweł Krakowiak, takeITeasy Academy | Oct-22 | English | $9.99 | 45m total length | https://www.udemy.com/course/100-exercises-python-programming-data-science-numpy/ | Paweł Krakowiak | Python Developer/Data Scientist/Stockbroker | 4.6 | 5319 | 235854 | 100+ Exercises - Python Programming - Data Science - NumPy Welcome to the 100+ Exercises - Python Programming - Data Science - NumPy course, where you can test your Python programming skills in data science, specifically in NumPy. Some topics you will find in the exercises: working with numpy arrays generating numpy arrays generating numpy arrays with random values iterating through arrays dealing with missing values working with matrices reading/writing files joining arrays reshaping arrays computing basic array statistics sorting arrays filtering arrays image as an array linear algebra matrix multiplication determinant of the matrix eigenvalues and eignevectors inverse matrix shuffling arrays working with polynomials working with dates working with strings in array solving systems of equations This course is designed for people who have basic knowledge in Python and NumPy package. It consists of 100 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course. If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today. Stack Overflow Developer Survey According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language with NumPy being the second most used tool in the "Other Frameworks and Libraries" category. Python passed SQL to become our third most popular technology. Python is the language developers want to work with most if they aren’t already doing so. | https://www.udemy.com/course/100-exercises-python-programming-data-science-numpy/#instructor-1 | EN Python Developer/Data Scientist/Stockbroker Founder at e-smartdata[.]org. Big fan of new technologies! Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Graduate of MA studies in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Stockbroker license holder (no 3073). Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management). PL Data Scientist, Securities Broker Założyciel platformy e-smartdata[.]org Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python oraz rozwiązań chmurowych. Absolwent podyplomowych studiów na Polsko-Japońskiej Akademii Technik Komputerowych na kierunku Informatyka, spec. Big Data. Absolwent studiów magisterskich z matematyki finansowej i aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadacz licencji Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Wykładowca w Fundacji GPW prowadzący szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Z doświadczeniem w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe. IG: e_smartdata | Python | Data Scientist | >=4 | Below 1K | >=50K | >=4 | Below 10 K | >=2 Lakh | |||||||||||||||||
Artificial Intelligence III - Deep Learning in Java | Deep Learning Fundamentals, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) + LSTM, GRUs | 4.7 | 166 | 2767 | Created by Holczer Balazs | Dec-21 | English | $9.99 | 4h 9m total length | https://www.udemy.com/course/artificial-intelligence-iii-in-java/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications - natural language processing and sentiment analysis! So you'll learn about the following topics: Section #1: multi-layer neural networks and deep learning theory activtion functions (ReLU and many more) deep neural networks implementation how to use deeplearning4j (DL4J) Section #2: convolutional neural networks (CNNs) theory and implementation what are kernels (feature detectors)? pooling layers and flattening layers using convolutional neural networks (CNNs) for optical character recognition (OCR) using convolutional neural networks (CNNs) for smile detection emoji detector application from scratch Section #3: recurrent neural networks (RNNs) theory using recurrent neural netoworks (RNNs) for natural language processing (NLP) using recurrent neural networks (RNNs) for sentiment analysis These are the topics we'll consider on a one by one basis. You will get lifetime access to over 40+ lectures! This course comes with a 30 day money back guarantee! If you are not satisfied in any way, you'll get your money back. Let's get started! | https://www.udemy.com/course/artificial-intelligence-iii-in-java/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Artificial Intelligence | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Data science and Data preparation with KNIME | KNIME - a powerful tool for data science and machine learning Data science with higher efficiency. KNIME data cleaning | 4.3 | 166 | 1278 | Created by Dan We | Nov-22 | English | $9.99 | 4h 21m total length | https://www.udemy.com/course/data-science-and-data-preparation-with-knime/ | Dan We | Coach | 4.5 | 13236 | 80496 | Data science and Data cleaning and Data preparation with KNIME Hello everyone hope you are doing fine. Let’s face it. Data preparation ,data cleaning, data preprocessing (whatever you want to call it) is most often the most tedious and time consuming work in the data science / data analysis area. So many people ask: How can we speed up the process and be more efficient? Well one option could be to use tools which allow us to speed up the process (and sometimes reduce the amount of code we need to write). Meet KNIME A great tool which comes to our rescue. KNIME allows us to do data preparation / data cleaning in a very appealing drag and drop interface. (No coding experience is required yet it still allows us if we want to use languages like R, Python or Java. So, we can code if we want but don’t have to!). The flexibility of KNIME makes that happen. WITH KNIME we can also do Data Science, so machine learning and AI with or without coding. And the best: The Desktop version is free! So, is it worth it to dive deeper into KNIME? ABSOLUTELY! This course is the second KNIME class and expands the knowledge you have acquired in the first class "KNIME - a crash course for beginners" which is also available on udemy. We do not cover the basics (e.g. the interface, basic data import and filter nodes,...) here. If you need to refresh your knowlege or you have not had the chance to learn the basics I would recommend to check the prior class first (which covers all the basics in a great case study!) In this class we dive into efficient ways to import multiple files into KNIME loops webscraping scripting (using Python code in KNIME) hyperparameter optimization feature selection basic machine learning workflows and helpful nodes for this in KNIME If that does not sound like fun, then what? So, if that is interesting to you then let’s get started! Are you ready? | https://www.udemy.com/course/data-science-and-data-preparation-with-knime/#instructor-1 | Dan is a 33 year old entrepreneur ,data enthusiast consultant and trainer. He holds a master degree and is certified in Power BI, Tableau, Alteryx (Core and Advanced) and KNIME (L1-L3). He is currently working in Business Intelligence field and helps companies and individuals to get key insights from their data to deliver long term growth and outpace their competitors. He has a passion for learning and teaching and is committed to support other people, by offering them educational services to help them accomplishing their goals and becoming the best in their profession or explore a new career path. "The dots will connect" Just do it! | Misc | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | ||||||||||||||||||
Introduction to AI, Machine Learning and Python basics | Learn to understand Artificial Intelligence and Machine Learning algorithms, and learn the basics of Python Programming | 4.4 | 163 | 4402 | Created by Timur Kazantsev | Oct-21 | English | $11.99 | 2h 37m total length | https://www.udemy.com/course/introduction-to-ai-machine-learning-and-python-basics/ | Timur Kazantsev | Teacher, Life-Long Learner, Traveller | 4.3 | 2937 | 28115 | Artificial Intelligence has already become an indispensable part of our everyday life, whether when we browse the Internet, shop online, watch videos and images on social networks, and even when we drive a car or use our smartphones. AI is widely used in medicine, sales forecasting, space industry and construction. Since we are surrounded by AI technologies everywhere, we need to understand how these technologies work. And for such understanding at a basic level, it is not necessary to have a technical or IT education. *** In this course, you will learn about the fundamental concepts of Artificial Intelligence and Machine learning. You will get acquainted with their main types, algorithms and models that are used to solve completely different problems. We will even create models together to solve specific practical examples in Excel - for those who do not want to program anything. And for those who want to get acquainted with Python , a programming language that solves more than 53% of all machine learning tasks today, in this course you will find lectures to familiarize yourself with the basics of programming in this language. ** This course may become a kind of springboard for your career development in the field of AI and Machine learning. Having mastered this short course, you will be able to choose the particular area in which you would like to develop and work further. It is worth mentioning that today, AI and Machine Learning specialists are among the highest paid and sought after on the market (according to various estimates, there are about 300,000 AI experts on the global market today, while the demand for them is several million). ** So why not reinforce your resume with a certificate from Udemy, the largest international educational platform , that you have completed this course on Artificial Intelligence and Machine Learning, and the basics of Python programming . *** After completing this course, you will be able to communicate freely on topics related to Artificial Intelligence, Machine and Deep Learning, and Neural Networks. You will be able to analyze and visualize data, use algorithms to solve problems from different areas. *** This course will be regularly supplemented with new lectures and after enrolling in it you will have full access to all materials without any restrictions. Spend a few hours studying this course to get new or improve existing skills and broaden your horizons using the acquired knowledge. See you inside the course! *** | https://www.udemy.com/course/introduction-to-ai-machine-learning-and-python-basics/#instructor-1 | I'm an MA graduate with degrees in International Relations and World Economy. Learning foreign languages, travelling and working in cosmopolitan environments has always been indispensable part of my life. My work experience includes working on challenging projects for government and private sector (security products, banking, investment) across 5 continents, including Africa, Middle East, South-East Asia and Americas, and internship at the UN Office and WTO in Geneva, Switzerland. I'm totally passionate about psychology, craftsmanship, motivation, personal finance and languages - I speak English, Russian, French, Turkish and a little Arabic, I also started learning Hungarian a while ago:). I'm keen on reading, sports, football, and playing the guitar. I'm always eager to enhance and learn new skills and strive for new knowledge. In my courses I try to combine basic theoretical knowledge with practical examples, and deliver them in reasonably short yet powerful, ready-to-implement lectures. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Data Science with R - Beginners | This training is an introduction to the concept of Data science and its application using R programming language | 3.5 | 163 | 16283 | Created by eduCode Forum | Dec-18 | English | $9.99 | 5h 47m total length | https://www.udemy.com/course/data-science-with-r-beginners/ | eduCode Forum | Code with us! | 3.5 | 446 | 80211 | This training is an introduction to the concept of Data science domain and its application using R programming language. The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. The tutorials will include the following; Introduction to R programming Reproducible Analysis Data Manipulation Visualizing Data Working with Large Datasets Supervised Learning Unsupervised Learning In depth R programming Object oriented Programming Building an R package Testing and Package Checking Version Control Profiling and Optimizing | https://www.udemy.com/course/data-science-with-r-beginners/#instructor-1 | Hello! We are eduCode Forum. We teach coding to engineering students and aspiring coders. Teaching is our passion. Teaching started very early in my career when I started guiding students during my school days. Later, I did my engineering in Computer Science and there too, I was teaching my friends. I love to learn new things and teach the same to the beginners. I love traveling, going to new places and meeting different people from different cultures. Our goal is to offer high quality technology courses which suits newbie as well as an expert. We focus on the technology which are essential to perform in today's job market. We believe anyone can code. We are a team of dedicated professionals who perform intense research, pragmatic planning and come up with easily understandable and quality courses for student around the world. We are fantastic content maker and fabulous presenters. Our training team guides 1000s of software developers yearly through courses in technologies. We offer students a full range of learning options by delivering software development training in classrooms, live online, on-site, and on-demand. | Misc | >=3 | Below 1K | >=15K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Sentiment Analysis with NLP using Python and Flask | Along with a Project | 3.8 | 163 | 21753 | Created by Yaswanth Sai Palaghat | Jan-21 | English | $9.99 | 1h 25m total length | https://www.udemy.com/course/sentiment-analysis-with-nlp-using-python-flask/ | Yaswanth Sai Palaghat | Founder of Techie Empire | 3.8 | 3546 | 159956 | Sentiment Analysis is widely used in the area of Machine Learning under Natural Language Processing. In this course, you will know how to use sentiment analysis on reviews with the help of a NLP library called TextBlob. You will learn and develop a Flask based WebApp that takes reviews from the user and perform sentiment analysis on the same. | https://www.udemy.com/course/sentiment-analysis-with-nlp-using-python-flask/#instructor-1 | Hey techies, I am Yaswanth Sai Palaghat. I am a content creator, Digital Marketer and a software developer from Hyderabad and a freelancer. I have a youtube channel named "YASWANTH SAI PALAGHAT" where I regularly upload videos on tech, career development, personal finance, Interview Preparation, and many more. | NLP | Founder/Entrepreneur | >=3 | Below 1K | >=20K | >=3 | Below 10 K | >=1.5 Lakh | |||||||||||||||||
NLTK: Build Document Classifier & Spell Checker with Python | NLP with Python - Analyzing Text with the Natural Language Toolkit (NLTK) - Natural Language Processing (NLP) Tutorial | 3.7 | 161 | 855 | Created by GoTrained Academy, Waqar Ahmed | Feb-19 | English | $9.99 | 5h 17m total length | https://www.udemy.com/course/natural-language-processing-python-nltk/ | GoTrained Academy | eLearning Professionals | 4 | 6291 | 73358 | This Natural Language Processing (NLP) tutorial covers core basics of NLP using the well-known Python package Natural Language Toolkit (NLTK). The course helps trainees become familiar with common concepts like tokens, tokenization, stemming, lemmatization, and using regex for tokenization or for stemming. It discusses classification, tagging, normalization of our input or raw text. It also covers some machine learning algorithms such as Naive Bayes. After taking this course, you will be familiar with the basic terminologies and concepts of Natural Language Processing (NLP) and you should be able to develop NLP applications using the knowledge you gained in this course. What is Natural Language Processing (NLP)? Natural language processing, or NLP for short, is the ability of a computer program to understand, manipulate, analyze, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and spam detection. What is NLTK? The Natural Language Toolkit (NLTK) is a suite of program modules and data-sets for text analysis, covering symbolic and statistical Natural Language Processing (NLP). NLTK is written in Python. Over the past few years, NLTK has become popular in teaching and research. NLTK includes capabilities for tokenizing, parsing, and identifying named entities as well as many more features. This Natural Language Processing (NLP) tutorial mainly cover NLTK modules. About the course This Natural Language Processing (NLP) tutorial is basically designed to make you understand the fundamental concepts of Natural Language Processing (NLP) with Python, and we will be learning some machine learning algorithms as well because natural language processing and machine learning move hand in hand as NLP employs machine learning techniques to learn and understand what a sentence is saying, or what a user has said and it sends an appropriate response back. So, by the end of this course, I hope you will have a clear idea, a clear view of the core fundamental concepts of NLP and how we can actually make applications using these core concepts. Looking forward to seeing you in the course. ---- Keywords: Natural Language Processing (NLP) tutorial; Python NLTK; Machine Learning; Sentiment Analysis; Data Mining; Text Analysis; Text Processing | https://www.udemy.com/course/natural-language-processing-python-nltk/#instructor-1 | GoTrained is an e-learning academy aiming at creating useful content in different languages and it concentrates on technology and management. We adopt a special approach for selecting content we provide; we mainly focus on skills that are frequently requested by clients and jobs while there are only few videos that cover them. We also try to build video series to cover not only the basics, but also the advanced areas. | Python | >=3 | Below 1K | Below 1K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Beginning with Machine Learning, Data Science and Python | Fundamentals of Data Science : Exploratory Data Analysis (EDA), Regression (Linear & logistic), Visualization, Basic ML | 4.4 | 161 | 7927 | Created by UNP United Network of Professionals | Jul-18 | English | $9.99 | 3h 38m total length | https://www.udemy.com/course/jumpstart-to-data-science-machine-learning-using-python/ | UNP United Network of Professionals | Publishing top-notch data science learning materials | 4.5 | 1368 | 25299 | 85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well. This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will go a long way, understand any method easily, and create your own predictive analytics models. At the end of this course, you will be able to: independently build machine learning and predictive analytics models confidently appear for exploratory data analysis, foundational data science, python interviews demonstrate mastery in exploratory data science and python demonstrate mastery in logistic and linear regression, the workhorses of data science This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications. Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method. This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on examples. Next, linear and logistic regression is discussed in detail and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next. Final learnings are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice. | https://www.udemy.com/course/jumpstart-to-data-science-machine-learning-using-python/#instructor-1 | We are a team of working professionals from around the globe ,about 30 strong, coming from various spheres of the Data Science Universe,each bringing in a unique set of skills which we have acquired through years of experience in almost every domain of Business.The Professionals in UNP are unified by a single common goal to minimise the entry barrier to quality education at every stage of one’s life and we strongly believe that knowledge should be shared in its truest form to transcend.We are committed to provide quality education in the realms of Data Sciences coupling it with IoT and Cloud Computing, DevOps, Quantum Computing & Blockchain At UNP- R&D emerging Tech are being nurtured and applied to create the first Decentralised Education Ecosystem, as we believe democratisation of knowledge & education is the Future. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Accelerate Deep Learning on Raspberry Pi | How to Accelerate your AI Object Detection Models 5X faster on a Raspberry Pi 3, using Intel Movidius for Deep Learning | 3.4 | 160 | 1331 | Created by Augmented Startups, Laszlo Benke | Dec-19 | English | $13.99 | 1h 58m total length | https://www.udemy.com/course/accelerate-deep-learning-on-raspberry-pi-movidius/ | Augmented Startups | M(Eng) AI Instructor 97k+ Subs on YouTube & 60k+ students | 3.8 | 3532 | 56203 | Learn how we implemented Deep Learning Object Detection Models on Raspberry Pi and accelerated them with Intel Movidius Neural Compute Stick. When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is, that image classification and object detection runs just fine on our expensive, power consuming and bulky Deep Learning machines. However, not everyone can afford or implement AI for their practical applications. This is when we went searching for an affordable, compact, less power hungry alternative. Generally if we'd want to shrink our IoT and automation projects, we'd often look to the Raspberry Pi which is versatile computing solution for numerous problems. This made us ponder about how we can port out deep learning models to this compact computing unit. Not only that, but how could we run it at close to real-time? Amongst the possible solutions we arrived at using the raspberry pi in conjunction with an AI Accelerator USB stick that was made by Intel to boost our object detection frame-rate. However it was not so simple to get it up and running. Implementing the documentation, we landed up with a series of bugs after bugs, which became a bit tedious. After endless posts on forums, tutorials and blogs, we have documented a seamless guide in the form of this course; which will show you, step-by-step, on how to implement your own Deep Learning Object Detection models on video and webcam without all the wasteful debugging. So essentially, we've structured this training to reduce debugging, speed up your time to market and get you results sooner. In this course, here's some of the things that you will learn: Getting Started with Raspberry Pi even if you are a beginner, Deep Learning Basics, Object Detection Models - Pros and Cons of each CNN, Setup and Install Movidius Neural Compute Stick (NCS) SDK, Currently, the OpenVINO is available for Raspbian, so the NCS2 is already compatible with the Raspberry Pi, but this course is mainly for the Movidius (NCS version 1). Run Yolo and Mobilenet SSD object detection models in recorded or live video You also get helpful bonuses: *OpenCV CPU inference *Introduction to Custom Model Training Personal help within the course I donate my time to regularly hold office hours with students. During the office hours you can ask me any business question you want, and I will do my best to help you. The office hours are free. I don't try to sell anything. Students can start discussions and message me with private questions. I answer 99% of questions within 24 hours. I love helping students who take my courses and I look forward to helping you. I regularly update this course to reflect the current marketing landscape. Get a Career Boost with a Certificate of Completion Upon completing 100% of this course, you will be emailed a certificate of completion. You can show it as proof of your expertise and that you have completed a certain number of hours of instruction. If you want to get a marketing job or freelancing clients, a certificate from this course can help you appear as a stronger candidate for Artificial Intelligence jobs. Money-Back Guarantee The course comes with an unconditional, Udemy-backed, 30-day money-back guarantee. This is not just a guarantee, it's my personal promise to you that I will go out of my way to help you succeed just like I've done for thousands of my other students. Let me help you get fast results. Enroll now, by clicking the button and let us show you how to develop Accelerated AI on Raspberry Pi. | https://www.udemy.com/course/accelerate-deep-learning-on-raspberry-pi-movidius/#instructor-1 | So a bit about me, Ritesh Kanjee: I've graduated from University of Johannesburg as an Electronic Engineer with a Masters in Image Processing and 8 years ago I started my online school called Augmented Startups where I have over 97'000 subscribers on YouTube and over 60'000 students on Augmented AI Bootcamp/Udemy. I’ve worked with popular tools such as TensorFlow Keras, Open CV, and PyTorch and I’ve also produced High ranking tutorials that feature on Google and YouTube. My Machine Learning Series is also one of the most viewed videos, over 300 thousand views and you’ll find them ranked right at the top on YouTube search results. From my tutorials, I have received a lot of great feedback and testimonials from students all around the world, I will share those reviews towards the end of the video And I have also presented at international conferences and meetups in AI. For industry standard AI, I have partnered up with Geeky Bee AI who are Experts in the field in AI and Deep Learning and have experience developing AI apps for real world applications. | Deep Learning | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Python for Data Analysis: step-by-step with projects | Learn Python for data analysis (pandas, data visualizations, statistics) with real world datasets and practice projects | 4.5 | 159 | 846 | Created by Lianne and Justin (Just into Data) | Oct-22 | English | $9.99 | 10h 59m total length | https://www.udemy.com/course/python-for-data-analysis-step-by-step/ | Lianne and Justin (Just into Data) | Data Scientists | 4.6 | 390 | 1854 | Welcome to your Python for data analysis course! This course offers 11 hours of HD video lectures, detailed code notebooks, 3 guided practice projects, based on multiple real-world datasets. This course will guide you to learn from scratch how to analyze data efficiently in Python. By following this course, you'll gain practical experience analyzing real-world datasets. So that by the end, you'll be able to conduct your own analysis with Python, and extract valuable insights that can transform your business! What are the design principles of the course? Instead of dumping all the available Python libraries or functions to you, we picked only the most useful ones based on our industry experience to cover in the course. This allows you to focus and master the foundations. The course is arranged in different sections based on the step-by-step process of REAL data analysis. Please check out the course overview lecture for details. Besides Python programming, you'll also get exposed to basic statistical knowledge necessary for data analysis. Combined with the detailed video lectures, you'll be given a few projects to work on to reinforce the knowledge. In the end, you'll have a solid foundation of data analysis, and be able to use Python for the whole process. Why data analysis in Python? Data analysis is a critical skill and is getting more popular. Nowadays, almost every organization has some data. Data could be very useful, but not without appropriate analysis. Data analysis enables us to transform data into insights for businesses, to make informative decisions. You can find data analysis being used in almost every industry, be it health care, finance, or technology. While Python is one of the employers' most in-demand skills for data science. It is not only easy to learn, but also very powerful. Who is this course for? This course is helpful for anyone interested in analyzing data effectively. Perhaps you want to become a data analyst or a data scientist, or maybe you just want the skills to work on your projects. This course is beginner-friendly. However, we recommend you to have some basic knowledge of Python or at least another programming language. With that said, there is a Python crash course included, so you can pick up or review the skills needed. What are the main Python libraries covered? Pandas Scikit-learn Seaborn All you need to start this course is the desire to learn, and a computer! Looking forward to seeing you inside the course! Cheers, Lianne and Justin Preview image designed by freepik | https://www.udemy.com/course/python-for-data-analysis-step-by-step/#instructor-1 | Justin: an experienced data scientist in many different fields, such as marketing, anti-money laundering, and big data technologies. He also has a bachelor’s degree in computer engineering and a master’s degree in statistics. Lianne: an experienced statistician who has worked in the central bank as well as commercial banks, where she monitored major financial institutions and conducted fraud analysis. She has both a bachelor’s and a master’s degree in statistics. | Python | Data Scientist | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Neural Networks With MATLAB: Programming For Beginners | Neural Network and Its Applications in Data Fitting Problems with MATLAB (ToolBox) | 4 | 158 | 9170 | Created by Karthik K | Mar-20 | English | $9.99 | 32m total length | https://www.udemy.com/course/neuralnetworkmatlab/ | Karthik K | Computational Engineer | 4.3 | 436 | 39220 | This course provides a comprehensive introduction to the neural network for the data fitting problems using MATLAB. Attendees will learn to construct, train, and simulate different kinds of neural networks. A set of practical problems are solved in this course. At the end of this course, you will be able to solve the Neural Network problems using the MATLAB - Neural Network Toolbox. The MATLAB scripts and functions included in the class are also available for download. Happy learning. NB: This course is designed most straightforwardly to utilise your time wisely. | https://www.udemy.com/course/neuralnetworkmatlab/#instructor-1 | Engineer dedicated to utilizing the power of Machine learning and Deep learning to solve real-world problems, improve design and performance assessment. Over ten years of experience in engineering and R&D environment. Engineering professional with a focus on Multi-physics CFD-ML from IIT Madras. Experienced in implementing action-oriented solutions to complex business problem. | Neural Networks | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Data science with R: tidyverse | R Programming Language, Data Analysis, Data Cleaning, Data Science, Data Wrangling, tidyverse, dplyr, ggplot2, RStudio | 4.5 | 157 | 1306 | Created by Marko Intihar | Oct-22 | English | $9.99 | 30h 48m total length | https://www.udemy.com/course/data-science-with-r-tidyverse/ | Marko Intihar | Data Scientist, Researcher and Teacher | 4.5 | 738 | 9027 | Data Science skills are still one of the most in-demand skills on the job market today. Many people see only the fun part of data science, tasks like: "search for data insight", "reveal the hidden truth behind the data", "build predictive models", "apply machine learning algorithms", and so on. The reality, which is known to most data scientists, is, that when you deal with real data, the most time-consuming operations of any data science project are: "data importing", "data cleaning", "data wrangling", "data exploring" and so on. So it is necessary to have an adequate tool for addressing given data-related tasks. What if I say, there is a freely accessible tool, that falls into the provided description above! R is one of the most in-demand programming languages when it comes to applied statistics, data science, data exploration, etc. If you combine R with R's collection of libraries called tidyverse, you get one of the deadliest tools, which was designed for data science-related tasks. All tidyverse libraries share a unique philosophy, grammar, and data types. Therefore libraries can be used side by side, and enable you to write efficient and more optimized R code, which will help you finish projects faster. This course includes several chapters, each chapter introduces different aspects of data-related tasks, with the proper tidyverse tool to help you deal with a given task. Also, the course brings to the table theory related to the topic, and practical examples, which are covered in R. If you dive into the course, you will be engaged with many different data science challenges, here are just a few of them from the course: Tidy data, how to clean your data with tidyverse? Grammar of data wrangling. How to wrangle data with dplyr and tidyr. Create table-like objects called tibble. Import and parse data with readr and other libraries. Deal with strings in R using stringr. Apply Regular Expressions concepts when dealing with strings. Deal with categorical variables using forcats. Grammar of Data Visualization. Explore data and draw statistical plots using ggplot2. Use concepts of functional programming, and map functions using purrr. Efficiently deal with lists with the help of purrr. Practical applications of relational data. Use dplyr for relational data. Tidy evaluation inside tidyverse. Apply tidyverse tools for the final practical data science project. Course includes: over 25 hours of lecture videos, R scripts and additional data (provided in the course material), engagement with assignments at the end of each chapter, assignments walkthrough videos (where you can check your results). All being said this makes one of Udemy's most comprehensive courses for data science-related tasks using R and tidyverse. Enroll today and become the master of R's tidyverse!!! | https://www.udemy.com/course/data-science-with-r-tidyverse/#instructor-1 | Hi, my name is Marko Intihar. I have a Ph.D. from Logistics from the University of Maribor, and years of professional experience as an instructor and an expert in the fields of applied Statistics, Data science and programming. During my active academic career, I have published several scientific papers in the domain of time series modeling. I began my professional career as a researcher and a teaching assistant at the university. Core teaching and research fields were statistics, data analysis, and operations research. After several years of working for the university, I have decided to test my theoretical knowledge and apply my skills for solving practical problems from the industry. I employed as a data scientist for the biggest Slovenian retail company. In retail tons of data are generated daily, therefore I was able to enhance my data mining and machine learning skills to deliver practical solutions for the company. After 4 years of working in retail, I decided to move to the financial sector. Currently, I am working as a data scientist in a bank. The banking industry carries different kinds of challenges as the retail industry, therefore work enables lots of substance for professional and personal growth. I am an enthusiastic data scientist is my private life, therefore my career and hobby go with one and another perfectly. It would be my pleasure to share some of my knowledge with you. | Misc | Researcher | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Python for Data Science and Data Analysis | Learn the Basics of Python, Numpy, Pandas, Matplotlib, Seaborn, Bokeh and Scikit-Learn | 4.2 | 157 | 744 | Created by AI Sciences, AI Sciences Team | Nov-22 | English | $9.99 | 16h 15m total length | https://www.udemy.com/course/python-mastering-python-for-data-science-from-zero-to-hero/ | AI Sciences | AI Experts & Data Scientists |4+ Rated | 160+ Countries | 4.5 | 2414 | 40750 | A comprehensive course that will teach you how to use the power of Python to solve real-world data science and data analysis problems. Welcome to Mastering Python for Data Science & Data Analysis! This real-time actionable course is suitable for all skill levels. A programming or statistical background is not mandatory for you to be successful in this course. But you learn Python best by doing. That’s the reason you have a series of mini projects in this course. This course will enable you to build a Data Science foundation, whether you have basic Python skills or not. The code-along and well planned-out exercises will make you comfortable with the Python syntax right from the outset. At the end of this short course, you’ll be proficient in the fundamentals of Python programming for Data Science and Data Analysis. In this truly step-by-step course, every new tutorial video is built on what you have already learned. The aim is to move you one extra step forward at a time, and then, you are assigned a small task that is solved right at the beginning of the next video. That is, you start by understanding the theoretical part of a new concept first. Then, you master this concept by implementing everything practically using Python. Become a Python developer and Data Scientist by enrolling in this course. Even if you are a novice in Python and data science, you will find this illustrative course informative, practical, and helpful. And if you aren’t new to Python and data science, you’ll still find the hands-on projects in this course immensely helpful. | https://www.udemy.com/course/python-mastering-python-for-data-science-from-zero-to-hero/#instructor-1 | We are a group of experts, PhDs and Practitioners of Artificial Intelligence, Computer Science, Machine Learning, and Statistics. Some of us work in big companies like Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. We decided to produce a series of courses mainly dedicated to beginners and newcomers on the techniques and methods of Machine Learning, Statistics, Artificial Intelligence, and Data Science. Initially, our objective was to help only those who wish to understand these techniques more easily and to be able to start without too much theory and without a long reading. Today we also publish a more complete course on some topics for a wider audience. Our courses have had phenomenal success. Our Courses have helped more than 100,000 students to master AI and Data Science. | Python | Data Scientist | >=4 | Below 1K | Below 1K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Deep Learning Bootcamp with 5 Capstone Projects | Learn about Deep Learning - ANN, CNN, RNN, LSTMs along with Real Time Capstone Projects | 4.2 | 157 | 18964 | Created by Data Is Good Academy | Mar-22 | English | $9.99 | 6h 33m total length | https://www.udemy.com/course/deep-learning-machine-learning/ | Data Is Good Academy | An Google, Facebook, Kaggle Grandmasters team | 4.3 | 7578 | 251843 | Are you ready to master Deep Learning skills? Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis, etc. Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very important for you that you should have a proper plan regarding that. Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of the Python programming language. If you want to build super-powerful applications in Deep Learning. Then, you are at the right place. This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning. The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets. This course will cover the following topics:- 1. Deep Learning (DL). 2. Artificial Neural Network (ANN). 3. Convolutional Neural Network (CNN). 4. Recurrent Neural Network. (RCN) 5. Learn to Implement the LSTMs. This course will take you through the basics to an advanced level in all the mentioned four topics. After taking this course, you will be confident enough to work independently on any projects on these topics. There are lots and lots of exercises for you to practice In this Deep Learning Course and also a 5 Bonus Deep Learning Project "Stock Market Prediction", "Fruits Identification System", "Face Expression Recognizer", "Detecting Pneumonia from Chest X-rays", and "Optimizing Crop Production". In this Optimizing Crop Production, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity. In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High-Level CNN Architectures. In this Stock Market Prediction project, you will learn to analyze, and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models, and different Statistical features. In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result. In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear, etc. to build a Facial Emotion Detector. Instructor Support - Quick Instructor Support for any queries. I'm looking forward to see you in the course! You will have access to all the resources used in this course. | https://www.udemy.com/course/deep-learning-machine-learning/#instructor-1 | We’re a bootstrapped, "indie" tech education company. Our vision is to Unlock human potential by transforming education and making it accessible and affordable to all. We are fiercely independent and proud with a sole focus to make world-class tech education a level playing field for anyone on this planet. Data is Good is on a Mission to create courses that will make our students learn the subject and fall in love with it & become lifelong passionate learners and explorers of that subject. | Deep Learning | Grandmaster | >=4 | Below 1K | >=15K | >=4 | Below 10 K | >=2.5 Lakh | |||||||||||||||||
Computer Vision - OCR using Python | Become a Computer Vision expert and learn optical character recognition - OCR using Tesseract, OpenCV and Deep Learning | 4.4 | 156 | 791 | Created by Vineeta Vashistha | Nov-22 | English | $9.99 | 6h 20m total length | https://www.udemy.com/course/computer-vision-ocr-using-python/ | Vineeta Vashistha | Technical Architect - Deep Learning | 4.3 | 338 | 1522 | Become an expert in extraction of Text from Image or Scanned Documents with the help of Computer Vision, OpenCV and Deep Learning concepts and develop yourself into Computer Vision - Optical Character Recognition (OCR) Specialist. Top 3 Reasons on why this course Computer Vision: OCR using Python stands-out among other courses: Inclusion of 5 in-demand projects of Computer Vision that have been explained through detailed code walkthrough and work seamlessly Dedicated In-Course Support is provided within 24 hours for any issues faced Comprehensive Coverage inclusive of theory and practical implementation of 2 Deep learning-based Text Detection models (CTPN and EAST) In this course, we are covering the complete life cycle of OCR which starts from Text Detection with OpenCV and Deep Learning Models in an Image to Text Recognition with Tesseract and then finally performing Text Labelling through Spacy and Regular Expression. Enroll in this course and become specialized in Computer Vision - OCR. Here is a summary of the key topics we will be learning and projects that we will design in the course: Installation Guide In this course we help students perform all basic operations and installation of required packages and software before they begin their learning journey. This includes installation of right package of Python, PyCharm installation together with suggestions around setting it up for running first project, installation of basic packages, tips and tricks around issues faced. Post this we are also covering complete guide to install Tesseract both on Windows and Ubuntu environment. Text Detection Techniques for OCR When we talk about Text detection, it is often approached by researchers with the help of multiple techniques. The foremost is letting the Text Recognition tool perform Text detection as well, however that’s too much relying on one software, the example of same is given in EasyOCR which are covering in our course. Moving on to advanced techniques, Text Detection is done with the help of most popular Deep Learning concept which is Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image with the help of convolutional feature maps connected by a recurrent neural network using VGG16. This has been described in detail in the course and also a pre-trained model for text detection along with detailed explanation of training customized on your own dataset is also provided. Now moving on to our next Text Detection Model, EAST (Efficient and Accurate Scene Text Detector) which is a deep learning-based algorithm that detects text with a single fully-convolutional neural network. EAST make use of ResNet-50 as its base model and along with pre-trained model, the detailed concept of how to perform training on your own dataset is also provided in the course. For students who face restriction in running code or installing PyCharm locally on their machines, we are also providing Google Colab version of executing the code of Text Detection for both CTPN and EAST. OCR Architecture In this course we are also covering complete architecture of OCR in detail to make students understand not only the concept but also on how to design various OCR based solutions for industrial use. Image Processing Concepts In Image processing, we are including Pixel, Kernels & Feature map along with OpenCV to deepen the understanding of OCR concepts well. Thereafter, we are explaining pre-processing techniques to optimize our Text detection which includes Binarization, Thresholding, Rescaling and also Noise Removal Techniques like Morphology, Dilation, Erosion, Blurring, Orientation, De-skewing, Borders & Perspective Transformation. While explaining image concepts we also cover how to perform image segmentation for better control and refined processing of image over small section. Text Recognition for OCR While performing text recognition we make use of Tesseract software and Pytesseract package and then update our existing code of CTPN and EAST to perform text recognition along with text detection. This helps in merging two steps into one and use it in optimized manner. In this course, we are also explaining in detail the various options that are available with PyTesseract to convert data on images to text format. For this section as well, we are providing both local and Google colab version of code of CTPN and EAST for execution on Pycharm and Colab. Name Entity Recognition (NER) In this section we are covering the next step involved in the processing of OCR after Text Recognition which is Text Labelling or in Machine Learning term we also call it as NER. There are two methods we are covering in the course for performing text labelling operation. First one is Regular Expression also called RegEx technique in which we identify patterns in the text and based on those patterns we identify and label various extracted text. Second technique is using Deep Learning Pre trained models provided by Spacy, these pretrained models have some patterns pre-defined and by making use of same we are able to categorize our text into different classes like Date, Name, Country etc and then use that as an output of OCR. Training Deep Learning Model – CTPN and EAST In the Model Training section of this course, we are explaining in detail, the steps involved in using user’s own data for more refined Text Detection with the help of Transfer Learning for both CTPN and EAST models. In this section we are making use of Google Colab to train both models on SIROE Dataset (It’s a text detection dataset available for training) Live Projects In the end of this course, we have chosen 5 in-demand projects of Computer Vision and are explaining them first through a Project Overview session and then through a detailed Code Walkthrough. The projects are: Number Plate Recognition - In this project we are identifying number plate in the input image and then make use of Tesseract to recognize the text and then we are storing these identified number plates in csv file with the help of Pandas Dataframe. Invoice Processing with Text Labelling - In this project we are taking Invoice scanned documents as input and by utilizing OCR techniques, we are converting them into text and towards the end we perform text labelling using Spacy model and regular expression. Invoice Processing with XML Output - In this project we are taking Invoice scanned documents as input and making use of Tesseract we are converting them into text and in the end, we are storing this data into structured format using XML, this is required so that we can integrate code with other application. Business Card Recognition - This project is an independent Flask based Web Application developed where we are asking users to upload Business card via web browser and then result is displayed on web page itself with detected text and its recognized values. KYC Digitization - KYC digitization is another Flask based Web Application where we are allowing users to upload Identification documents. This project makes use of CTPN Deep learning techniques to identify the text present in input image, the output detected text image is displayed along with recognized text for each block. This recognized text on right hand side is editable and user can choose which values to keep and can also update label names live on web page itself. Once all values are filtered and labelled you can also download same in excel format to your local machine. | https://www.udemy.com/course/computer-vision-ocr-using-python/#instructor-1 | Machine Learning and Deep Learning Architect with 18+ years of IT experience in developing algorithms and machine learning solutions across Finance, Healthcare, Retail and Travel domains. Most recently, I have worked as Technical Architect for Deep Learning and now I have started my own startup in the field of Artificial Intelligence. | Computer Vision | Architect | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Build 75 Powerful Data Science & Machine Learning Projects | Build & Deploy Data Science, Machine Learning, Deep Learning (Python, Flask, Django, AWS, Azure, GCP, Heruko Cloud) | 4.5 | 156 | 2533 | Created by Pianalytix . | Mar-22 | English | $9.99 | 73h 36m total length | https://www.udemy.com/course/real-world-data-science-projects-practically/ | Pianalytix . | Technology For Innovators | 4.6 | 1350 | 65362 | In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud). According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary. This makes Data Science a highly lucrative career choice. It is mainly due to the dearth of Data Scientists resulting in a huge income bubble. Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics, and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market. A Data Scientist enjoys a position of prestige in the company. The company relies on its expertise to make data-driven decisions and enable them to navigate in the right direction. Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales. A healthcare company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize. Still, the pay scale of Data scientists is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills. Due to several lucrative perks, Data Science is an attractive field. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career. In This Course, We Are Going To Work On 75 Real World Data Science, Machine Learning Projects Listed Below: Project-1: Pan Card Tempering Detector App -Deploy On Heroku Project-2: Dog breed prediction Flask App Project-3: Image Watermarking App -Deploy On Heroku Project-4: Traffic sign classification Project-5: Text Extraction From Images Application Project-6: Plant Disease Prediction Streamlit App Project-7: Vehicle Detection And Counting Flask App Project-8: Create A Face Swapping Flask App Project-9: Bird Species Prediction Flask App Project-10: Intel Image Classification Flask App Project-11: Language Translator App Using IBM Cloud Service -Deploy On Heroku Project-12: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku Project-13: Laptop Price Predictor -Deploy On Heroku Project-14: WhatsApp Text Analyzer -Deploy On Heroku Project-15: Course Recommendation System -Deploy On Heroku Project-16: IPL Match Win Predictor -Deploy On Heroku Project-17: Body Fat Estimator App -Deploy On Microsoft Azure Project-18: Campus Placement Predictor App -Deploy On Microsoft Azure Project-19: Car Acceptability Predictor -Deploy On Google Cloud Project-20: Book Genre Classification App -Deploy On Amazon Web Services Project 21 : DNA classification Deep Learning for finding E.Coli -AWS - Deploy On AWS Project 22 : Predict the next word in a sentence. - AWS - Deploy On AWS Project 23 : Predict Next Sequence of numbers using LSTM - AWS - Deploy On AWS Project 24 : Keyword Extraction from text using NLP - Deploy On Azure Project 25 : Correcting wrong spellings (correct spelling prediction) - Deploy On Azure Project 26 : Music popularity classififcation - Deploy On Google App Engine Project 27 : Advertisement Classification - Deploy On Google App Engine Project 28 : Image Digit Classification - Deploy On AWS Project 29 : Emotion Recognition using Neural Network - Deploy On AWS Project 30 : Breast cancer Classification - Deploy On AWS Project-31: Sentiment Analysis Django App -Deploy On Heroku Project-32: Attrition Rate Django Application Project-33: Find Legendary Pokemon Django App -Deploy On Heroku Project-34: Face Detection Streamlit App Project-35: Cats Vs Dogs Classification Flask App Project-36: Customer Revenue Prediction App -Deploy On Heroku Project-37: Gender From Voice Prediction App -Deploy On Heroku Project-38: Restaurant Recommendation System Project-39: Happiness Ranking Django App -Deploy On Heroku Project-40: Forest Fire Prediction Django App -Deploy On Heroku Project-41: Build Car Prices Prediction App -Deploy On Heroku Project-42: Build Affair Count Django App -Deploy On Heroku Project-43: Build Shrooming Predictions App -Deploy On Heroku Project-44: Google Play App Rating prediction With Deployment On Heroku Project-45: Build Bank Customers Predictions Django App -Deploy On Heroku Project-46: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku Project-47: Build Medical Cost Predictions Django App -Deploy On Heroku Project-48: Phishing Webpages Classification Django App -Deploy On Heroku Project-49: Clothing Fit-Size predictions Django App -Deploy On Heroku Project-50: Build Similarity In-Text Django App -Deploy On Heroku Project-51 : Sonic wave velocity prediction using Signal Processing Techniques Project-52 : Estimation of Pore Pressure using Machine Learning Project-53 : Audio processing using ML Project-54 : Text characterisation using Speech recognition Project-55 : Audio classification using Neural networks Project-56 : Developing a voice assistant Project-57 : Customer segmentation Project-58 : FIFA 2019 Analysis Project-59 : Sentiment analysis of web scrapped data Project-60 : Determing Red Vine Quality Project-61: Heart Attack Risk Prediction Using Eval ML (Auto ML) Project-62: Credit Card Fraud Detection Using Pycaret (Auto ML) Project-63: Flight Fare Prediction Using Auto SK Learn (Auto ML) Project-64: Petrol Price Forecasting Using Auto Keras Project-65: Bank Customer Churn Prediction Using H2O Auto ML Project-66: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML) Project-67: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML) Project-68: Pizza Price Prediction Using ML And EVALML(Auto ML) Project-69: IPL Cricket Score Prediction Using TPOT (Auto ML) Project-70: Predicting Bike Rentals Count Using ML And H2O Auto ML Project-71: Concrete Compressive Strength Prediction Using Auto Keras (Auto ML) Project-72: Bangalore House Price Prediction Using Auto SK Learn (Auto ML) Project-73: Hospital Mortality Prediction Using PyCaret (Auto ML) Project-74: Employee Evaluation For Promotion Using ML And Eval Auto ML Project-75: Drinking Water Potability Prediction Using ML And H2O Auto ML The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires. | https://www.udemy.com/course/real-world-data-science-projects-practically/#instructor-1 | Pianalytix Edutech Pvt Ltd uses cutting-edge AI technology & innovative product design to help users learn Machine Learning more efficiently and to implement Machine Learning in the real world. Pianalytix also leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by optimizing processes, maximizing efficiency and increasing profitability. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
A to Z (NLP) Machine Learning Model building and Deployment. | Python, Docker, Flask, GitLab, Jenkins tools and technology used for deploy model in your Local server. A complete Guide | 4.2 | 155 | 13915 | Created by Mohammed Rijwan | Nov-20 | English | $9.99 | 4h 23m total length | https://www.udemy.com/course/a-to-z-nlp-machine-learning-model-building-and-deployment/ | Mohammed Rijwan | Machine Learning Engineer | 4.2 | 155 | 13915 | Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it. Most of the problems nowadays as I have made a machine-learning model but what next. How it is available to the end-user, the answer is through API, but how it works? How you can understand where the Docker stands and how to monitor the build we created. This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools. This course has been designed into Following sections: 1) Configure and a quick walkthrough of each of the tools and technologies we used in this course. 2) Building our NLP Machine Learning model and tune the hyperparameters. 3) Creating flask API and running the WebAPI in our Browser. 4) Creating the Docker file, build our image and running our ML Model in Docker container. 5) Configure GitLab and push your code in GitLab. 6) Configure Jenkins and write Jenkins's file and run end-to-end Integration. This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it. | https://www.udemy.com/course/a-to-z-nlp-machine-learning-model-building-and-deployment/#instructor-1 | Hi, I am a working professional having experience in fields like ML, AI, DevOps tools like Docker, Jenkins, Git or GitLab, Python, C, C++, etc. I have worked in a much different organisation as a tech trainer and currently working as a Big Data and Machine Learning Engineer. My aim is to spread the knowledge which is an industrial standard and help you to find a better geek in you which will help you to make very robust projects which are highly in demand in the industries. I am sure my courses will help you to achieve your goals. Please visit my YouTube channel for more info. Cheer! Happy learning 🙂 | NLP | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Keras: Deep Learning in Python | Build complex deep learning algorithms easily in Python | 4 | 154 | 1063 | Created by Francisco Juretig | Jul-17 | English | $10.99 | 10h 4m total length | https://www.udemy.com/course/keras-deep-learning-in-python/ | Francisco Juretig | Mr | 3.9 | 452 | 24271 | Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math. The student is required to be familiar with Python, and machine learning; Some general knowledge on statistics and probability is recommended, but not strictly necessary. Among the many examples presented here, we use neural networks to tag images belonging to the River Thames, or the street; to classify edible and poisonous mushrooms, to predict the sales of several video games for multiple regions, to identify bolts and nuts in images, etc. We use most of our examples on Windows, but we show how to set up an AWS machine, and run our examples there. In terms of the course curriculum, we cover most of what Keras can actually do: such as the Sequential model, the model API, Convolutional neural nets, LSTM nets, etc. We also show how to actually bypass Keras, and build the models directly in Theano/Tensorflow syntax (although this is quite complex!) After taking this course, you should feel comfortable building neural nets for time sequences, images classification, pure classification and/or regression. All the lectures here can be downloaded and come with the corresponding material. | https://www.udemy.com/course/keras-deep-learning-in-python/#instructor-1 | I worked for 7+ years exp as statistical programmer in the industry. Expert in programming, statistics, data science, statistical algorithms. I have wide experience in many programming languages. Regular contributor to the R community, with 3 published packages. I also am expert SAS programmer. Contributor to scientific statistical journals. Latest publication on the Journal of Statistical Software. | Deep Learning | >=4 | Below 1K | Below 10K | >=3 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Natural Language Processing (NLP) with Python and NLTK | Practical Approach : Collecting and Preprocessing text data, Data Visualization, Model Building and NLP Apps | 4.4 | 154 | 1201 | Created by CARLOS QUIROS | Jul-19 | English | $9.99 | 7h 14m total length | https://www.udemy.com/course/natural-language-processing-with-python-and-nltk/ | CARLOS QUIROS | Industrial Engineer and Data Scientist | 4 | 1265 | 10286 | Natural Language Processing (NLP) is a hot topic into the Machine Learning field. This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications. Hot topics on NLP that I will cover with practical applications on this course are: - Regular expressions - Scrapping the web - Textract library for extracting text content - Sentence splitter and tokenization - Stemming and Lemmatization - Stop and rare word removal - Part of Speech (POS) tagging - Chunking - N-grams - Bag of Words: TfidfVectorizer - Frequency Chart - Co-occurence matrix - Word cloud library - Text similarity - Text clustering - Latent Semantic Analysis - Topic Modeling - Text Classification - Sentiment Analysis - Word2Vec library - Recommender Systems: Collaborative Filtering - Spam detector app - Social Media Mining on Twitter and much more!... In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library. Finally this course offers you many datasets and other resources for your practice and study. The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: [email protected] or by Twitter: @AILearningCQ | https://www.udemy.com/course/natural-language-processing-with-python-and-nltk/#instructor-1 | Industrial Engineer with more than 20 years in developing and managing business, with vast experience on process analysis and developing business information systems for data science. He has an Industrial Engineering degree from Pontificia Universidad Catolica del Peru (Lima-Peru) and Master in Business Administration (MBA) from ESAN Graduated School of Business (Lima-Peru). He is also an experience developer of machine learning and data science models in many fields of the industry and services like Marketing, Logistics, Finance, Manufacture, Quality Control, Computer Vision, NLP, Deep Learning apps and many others. He wants to share his experience teaching you on a simple and practical way, illustrating concepts based on graphics for better understanding. | NLP | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Introduction to Python For Data Science 2021 | A Quick and Easy Introduction into Python Programming for Data Science. Step by Step Guide | 4.1 | 153 | 12577 | Created by Eftekher Husain | Aug-20 | English | $9.99 | 1h 9m total length | https://www.udemy.com/course/introduction-to-python-for-data-science-g/ | Eftekher Husain | Software Engineer | 4 | 263 | 23017 | Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. If you need a quick brush-up on learning Python for the first time, you've come to the right place! Learning Python programming is one of the easiest right now. There's no need to worry if you haven't coded before. By the time you finish this course, you'll be comfortable with Python! Python is a great and friendly language to use and learn. Its fun, and can be adapted to both small and large projects. Python will cut your development time greatly and overall save you from a lot of unnecessary hassle. It is much faster to write Python than other languages. This course will be a quick way to understand all the basic concepts of Python programming. And then soon enough, You'll be a pro in no time. This course is a one-stop-shop to get started with Python, along with a few incentives. We'll begin with the basics of Python, learning about strings, variables, and getting to know the data types. We'll soon move on to the List and Functions in Python. Afterward, we'll be discussing NumPy. By then, you'll know all the basics of Python. After this course, you will be ready to jump in to advance python, and also you can dive into Data Science effortlessly. I hope you're excited to dive into the World of Python with this course. Well, what are you waiting for? Let's get started! | https://www.udemy.com/course/introduction-to-python-for-data-science-g/#instructor-1 | "The only person you are destined to become is the person you decide to be." -Ralph Waldo Emerson Eftekher Husain is a Software Engineer who is passionate about teaching. He has a Bachelor of Science Degree in Computer Science from The City College of New York. His vision is to help others reach their potential. | Python | Engineer/Developer | >=4 | Below 1K | >=10K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Data Science:Hands-on Diabetes Prediction with Pyspark MLlib | Diabetes Prediction using Machine Learning in Apache Spark | 4.1 | 152 | 11829 | Created by School of Disruptive Innovation | Sep-20 | English | $9.99 | 46m total length | https://www.udemy.com/course/data-science-hands-on-diabetes-prediction-with-pyspark-mllib/ | School of Disruptive Innovation | Creative Learning Solutions for the Digital Age | 4.1 | 1049 | 42339 | Would you like to build, train, test and evaluate a machine learning model that is able to detect diabetes using logistic regression? This is a Hands-on Machine Learning Course where you will practice alongside the classes. The dataset will be provided to you during the lectures. We highly recommend that for the best learning experience, you practice alongside the lectures. You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures. Learn the most important aspect of Spark Machine learning (Spark MLlib) : Pyspark fundamentals and implementing spark machine learning Importing and Working with Datasets Process data using a Machine Learning model using spark MLlib Build and train Logistic regression model Test and analyze the model The entire course has been divided into tasks. Each task has been very carefully created and designed to give you the best learning experience. In this hands-on project, we will complete the following tasks: Task 1: Project overview Task 2: Intro to Colab environment & install dependencies to run spark on Colab Task 3: Clone & explore the diabetes dataset Task 4: Data Cleaning Task 5: Correlation & feature selection Task 6: Build and train Logistic Regression Model using Spark MLlib Task 7: Performance evaluation & Test the model Task 8: Save & load model About Pyspark: Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics. Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real-Time Streaming Analytics. In other words, it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data. We will be using Big data tools in this project. Make a leap into Data science with this Spark MLlib project and showcase your skills on your resume. Click on the “ENROLL NOW” button and start learning. Happy Learning. | https://www.udemy.com/course/data-science-hands-on-diabetes-prediction-with-pyspark-mllib/#instructor-1 | Welcome to the School of the Disruptive Innovation. We are here to teach you what they don't teach you in school. We are unconventional in our ways but we promise and we over-deliver. We have a community of over 40,000+ students and 60,000+ enrollments across 166 countries. We offer courses on Data Science (Classical machine Learning, Deep learning, BigData, Data Visualization & Analysis), Android Development, Web Development, and Graphics Design. Every course is created and delivered by professionals in the field such as Technology related courses by software engineers and business related courses are created by business experts. | Spark | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
R Programming Complete Certification Training | R concepts, coding examples. Data structure, loops, functions, packages, plots/charts, data/files, decision-making in R. | 4 | 152 | 14343 | Created by Uplatz Training | Jul-20 | English | $9.99 | 20h 45m total length | https://www.udemy.com/course/r-programming-training/ | Uplatz Training | Fastest growing Global IT Training Provider | 3.7 | 12189 | 381859 | A warm welcome to the R Programming course by Uplatz. R is a programming language that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. While there is something called the S language which is often the vehicle of choice for research in statistical methodology, on the other hand R provides an Open Source route to participation in that activity. R is nothing but an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities. R can be considered as an integrated version of a programming language and software environment for statistical analysis, graphics representation and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. This programming language was named R, based on the first letter of first name of the two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs Language S. This R Programming course by Uplatz is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming. Even if you are a beginner, this R course is the perfect place to start. If you are trying to understand the R programming language as a beginner, this R Programming course will provide you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise. This R tutorial will provide you an opportunity to take a deep-dive into R programming and build your R skills from scratch. To get the most out of the R programming training, you would need to practice as you proceed with the tutorials. After successful completion of the R Programming training course you will be able to: Master the use of the R and RStudio interactive environment Expand R by installing R packages Explore and understand how to use the R documentation Read Structured Data into R from various sources Understand the different data types in R Understand the different data structures in R R programming constructs - variables, functions, string manipulation, loops, etc. Conduct decision-making using R Able to do Data and file management in R Packages in R Plotting and Visualization in R and more... R Programming - Course Syllabus 1. Fundamentals of R Language Introduction to R History of R Why R programming Language Comparison between R and Python Application of R 2. Setup of R Language Local Environment setup Installing R on Windows Installing R on Linux RStudio What is RStudio? Installation of RStudio First Program - Hello World 3. Variables and Data Types Variables in R Declaration of variable Variable assignment Finding variable Data types in R Data type conversion R programs for Variables and Data types in RStudio 4. Input-Output Features in R scan() function readline() function paste() function paste0() function cat() function R Programs for implementing these functions in RStudio 5. Operators in R Arithmetic Operators Relational Operators Logical Operators Assignment Operators Miscellaneous Operators R Programs to perform various operations using operators in RStudio 6. Data Structure in R (part-I) What is data structure? Types of data structure Vector - What is a vector in R? - Creating a vector - Accessing element of vector - Some more operations on vectors - R Programs for vectors in RStudio Application of Vector in R List - What is a list in R? - Creating a list - Accessing element of list - Modifying element of list - Some more operations on list R Programs for list in RStudio 7. Data Structure in R (part-II) Matrix or Matrices - What is matrix in R? - Creating a matrix - Accessing element of matrix - Modifying element of matrix - Matrix Operations R Programs for matrices in RStudio Application of Matrices in R Arrays - What are arrays in R? - Creating an array - Naming rows and columns - Accessing element of an array - Some more operations on arrays R Programs for arrays in RStudio 8. Data Structure in R (part-III) Data frame - What is a data frame in R? - Creating a data frame - Accessing element of data frame - Modifying element of data frame - Add the new element or component in data frame - Deleting element of data frame - Some more operations on data frame R Programs for data frame in RStudio Factors - Factors in R - Creating a factor - Accessing element of factor - Modifying element of factor R Programs for Factors in RStudio Application of Factors in R 9. Decision Making in R Introduction to Decision making Types of decision-making statements Introduction, syntax, flowchart and programs for - if statement - if…else statement - if…else if…else statement - switch statement 10. Loop control in R Introduction to loops in R Types of loops in R - for loop - while loop - repeat loop - nested loop break and next statement in R Introduction, syntax, flowchart and programs for - for loop - while loop - repeat loop - nested loop 11. Functions in R Introduction to function in R Built-in Function User-defined Function Creating a Function Function Components Calling a Function Recursive Function Various programs for functions in RStudio 12. Strings in R Introduction to string in R - Rules to write R Strings - Concatenate two or more strings in R - Find length of String in R - Extract Substring from a String in R - Changing the case i.e. Upper to lower case and lower to upper case Various programs for String in RStudio 13. Packages in R Introduction to Packages in R Get the list of all the packages installed in RStudio Installation of the packages How to use the packages in R Useful R Packages for Data Science R program for package in RStudio 14. Data and File Management in R Getting and Setting the Working Directory Input as CSV File Analysing the CSV File Writing into a CSV File R programs to implement CSV file 15. Plotting in R (Part-I) Line graph Scatterplots Pie Charts 3D Pie Chart 16. Plotting in R (Part-II) Bar / line chart Histogram Box plot | https://www.udemy.com/course/r-programming-training/#instructor-1 | Uplatz is UK-based leading IT Training provider serving students across the globe. Our uniqueness comes from the fact that we provide online training courses at a fraction of the average cost of these courses in the market. Over a short span of 3 years, Uplatz has grown massively to become a truly global IT training provider with a wide range of career-oriented courses on cutting-edge technologies and software programming. Our specialization includes Data Science, Data Engineering, SAP, Oracle, Salesforce, AWS, Microsoft Azure, Google Cloud, IBM Cloud, SAS, Python, R, JavaScript, Java, Full Stack Web Development, Mobile App Development, BI & Visualization, Tableau, Power BI, Spotfire, Data warehousing, ETL tools, Informatica, IBM Data Stage, Digital Marketing, Agile, DevOps, and more. Founded in March 2017, Uplatz has seen phenomenal rise in the training industry starting with an online course on SAP FICO and now providing training on 5000+ courses across 103 countries having served 300,000 students in a period of just 3 years. Uplatz's training courses are highly structured, subject-focused, and job-oriented with strong emphasis on practice and assignments. Our courses are designed and taught by more than a thousand highly skilled and experienced tutors who have strong expertise in their areas whether it be AWS, Azure, Adobe, SAP, Oracle, or any other technology or in-demand software. | Misc | >=4 | Below 1K | >=10K | >=3 | Below 1 Lakh | >=3.5 Lakh | ||||||||||||||||||
Data Science: Beginners Guide to the Command Line | Master the Basics of the Command Line and Prepare for a Career in Data Science! | 3.8 | 150 | 8042 | Created by Ben Weinstein | Oct-17 | English | $9.99 | 1h 21m total length | https://www.udemy.com/course/data-science-beginners-guide-to-the-command-line/ | Ben Weinstein | Data Scientist | Adventurer | 3.8 | 150 | 8042 | "I used to avoid my bash shell and go straight to my GUI. This course has taught me that it is pretty easy (and even necessary for any aspiring Data Scientist ) to use!" ~ Vanessa "This is the best course I have taken on Udemy and your style of teaching is super amazing." ~ Albert "Very well explained." ~ Bipen Welcome to Data Science: Beginners Guide to the Command Line! If you are interested in kick starting your career in Data Science, then this course is for you! This course will guide you through an array of topics concerning why the command line is a necessary tool for Data Scientists, an introduction to the Unix filesystem structure, and the basic shell commands that Data Scientists must master in order to effectively operate from the command line. My course is broken up into four main topics: Introduction, Command Line Basics, Command Line Advanced Topics, and Data Science From the Command Line. Introduction- In this section I will cover topics including, but not limited to, why the command line is a necessary tool for Data Scientists, a brief comparison between the Unix and Linux operating systems, and how to choose the correct shell environment.Command Line Basics- In this section I will cover topics including, but not limited to, a quick guide to shortcuts within the macOS Graphical User Interface, an introduction to the Unix filesystem structure, and the basics of executing shell commands.Command Line Advanced Topics- In this section I will cover common file types Data Scientists must be familiar with along with advanced shell commands.Data Science From the Command Line– In this section I will cover how to create, open, run, and save IPython scripts from the command line. In addition, I will go through an entire Data Science workflow from opening the terminal to completing a small analysis of a data set. | https://www.udemy.com/course/data-science-beginners-guide-to-the-command-line/#instructor-1 | Hi there! My name is Ben Weinstein and I am a Data Scientist and Adventurer. I hold an undergraduate degree in Neuroscience from Pitzer College and have thru-hiked both the Appalachian Trail (2013) and Pacific Crest Trail (2014). In total, I have hiked just shy of 5,000 miles through 17 states. My passion for data began in college when, for my senior thesis, I had the opportunity to work with a team of graduate researchers studying the neuroscience of how people make decisions under stress. Since then, I have been hooked on data! Professionally, I have worked as a hedge fund trader and operations analyst where I have spent the majority of my career creating models and sharing my insights with stakeholders. My decision to start teaching Data Science was born out of a love for learning but a frustration in finding concise, consolidated, and relevant content. I understand the urgency and frustration students face when trying to learn a new subject, and this is what drives my commitment to deliver the highest quality content possible. Whether your goal is to learn Pandas or command line SQL, I am here to provide you with the information that you will need to succeed. I am humbled to be your instructor and look forward to sharing my passion for Data Science with all my students! | Misc | Data Scientist | >=3 | Below 1K | Below 10K | >=3 | Below 1 K | Below 10 K | |||||||||||||||||
Apache Spark 2.0 + Scala : DO Big Data Analytics & ML | Project Based, Hands-on Practices, Spark Streaming, Scala Setup and building real world applications, Final project | 3.8 | 150 | 1204 | Created by V2 Maestros, LLC | Jan-17 | English | $9.99 | 7h 30m total length | https://www.udemy.com/course/apache-spark-scala-do-big-data-analytics-ml/ | V2 Maestros, LLC | Big Data / Data Science Experts | 50K+ students | 4.2 | 4162 | 76176 | Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This course teaches you Apache Spark 2.0 with Scala, trains you in building Spark Analytics and machine learning programs and helps you practice hands-on (2K LOC code samples !) with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable. Scala is a hot new technology used to build industry-grade applications and combining that with Spark gives you unlimited ability to build cutting edge applications. Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to good jobs, better quality of work and the best remuneration packages. The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you Learn solid fundamentals of the domain See demos, train and execute solid examples Practice hands-on and validate it with solutions provided Apply knowledge you acquired in an end-to-end real life project Taught by an expert in the field, you will also get prompt response to your queries and excellent support from Udemy. | https://www.udemy.com/course/apache-spark-scala-do-big-data-analytics-ml/#instructor-1 | V2 Maestros is dedicated to teaching big data / data science courses to students all over the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building Big Data and Data Science talent across the world. | Data Analyst | >=3 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Computer Vision In Python! Face Detection & Image Processing | Learn Computer Vision With OpenCV In Python! Master Python By Implementing Face Recognition & Image Processing In Python | 4.2 | 150 | 16866 | Created by Emenwa Global, Zoolord Academy | Nov-22 | English | $9.99 | 10h 58m total length | https://www.udemy.com/course/computer-vision-in-python-face-detection-image-processing/ | Emenwa Global | Senior Developers | 4 | 1971 | 143093 | Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems. Distinctions The fields most closely related to computer vision are image processing, image analysis and machine vision. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. Computer graphics produces image data from 3D models, computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines, e.g., as explored in augmented reality. The following characterizations appear relevant but should not be taken as universally accepted: Image processing and image analysis tend to focus on 2D images, how to transform one image to another, e.g., by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither require assumptions nor produce interpretations about the image content. Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images, e.g., how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image. Machine vision is the process of applying a range of technologies & methods to provide imaging-based automatic inspection, process control and robot guidance in industrial applications. Machine vision tends to focus on applications, mainly in manufacturing, e.g., vision-based robots and systems for vision-based inspection, measurement, or picking (such as bin picking). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasized by means of efficient implementations in hardware and software. It also implies that the external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms. There is also a field called imaging which primarily focuses on the process of producing images, but sometimes also deals with processing and analysis of images. For example, medical imaging includes substantial work on the analysis of image data in medical applications. Finally, pattern recognition is a field which uses various methods to extract information from signals in general, mainly based on statistical approaches and artificial neural networks. A significant part of this field is devoted to applying these methods to image data. Applications Applications range from tasks such as industrial machine vision systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, the computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for: Automatic inspection, e.g., in manufacturing applications; Assisting humans in identification tasks, e.g., a species identification system Controlling processes, e.g., an industrial robot; Detecting events, e.g., for visual surveillance or people counting, e.g., in the restaurant industry; Interaction, e.g., as the input to a device for computer-human interaction; Modeling objects or environments, e.g., medical image analysis or topographical modeling; Navigation, e.g., by an autonomous vehicle or mobile robot; and Organizing information, e.g., for indexing databases of images and image sequences. Medicine One of the most prominent application fields is medical computer vision, or medical image processing, characterized by the extraction of information from image data to diagnose a patient. An example of this is detection of tumors, arteriosclerosis or other malign changes; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information: e.g., about the structure of the brain, or about the quality of medical treatments. Applications of computer vision in the medical area also includes enhancement of images interpreted by humans—ultrasonic images or X-ray images for example—to reduce the influence of noise. Machine Vision A second application area in computer vision is in industry, sometimes called machine vision, where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects. Another example is measurement of position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in agricultural process to remove undesirable food stuff from bulk material, a process called optical sorting. Military Military applications are probably one of the largest areas for computer vision. The obvious examples are detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability. Autonomous vehicles One of the newer application areas is autonomous vehicles, which include submersibles, land-based vehicles (small robots with wheels, cars or trucks), aerial vehicles, and unmanned aerial vehicles (UAV). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, e.g. for knowing where it is, or for producing a map of its environment (SLAM) and for detecting obstacles. It can also be used for detecting certain task specific events, e.g., a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for autonomous driving of cars, but this technology has still not reached a level where it can be put on the market. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision, e.g., NASA's Curiosity and CNSA's Yutu-2 rover. Tactile Feedback Materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting micro undulations and calibrating robotic hands. Rubber can be used in order to create a mold that can be placed over a finger, inside of this mold would be multiple strain gauges. The finger mold and sensors could then be placed on top of a small sheet of rubber containing an array of rubber pins. A user can then wear the finger mold and trace a surface. A computer can then read the data from the strain gauges and measure if one or more of the pins is being pushed upward. If a pin is being pushed upward then the computer can recognize this as an imperfection in the surface. This sort of technology is useful in order to receive accurate data of the imperfections on a very large surface. Another variation of this finger mold sensor are sensors that contain a camera suspended in silicon. The silicon forms a dome around the outside of the camera and embedded in the silicon are point markers that are equally spaced. These cameras can then be placed on devices such as robotic hands in order to allow the computer to receive highly accurate tactile data. Other application areas include: Support of visual effects creation for cinema and broadcast, e.g., camera tracking (matchmoving). Surveillance. Driver drowsiness detection Tracking and counting organisms in the biological sciences [Reference: Wikipedia] | https://www.udemy.com/course/computer-vision-in-python-face-detection-image-processing/#instructor-1 | 100,000+ Students Have Built Their Skills And Industry Career With Our Professional Courses. Many Work In High Tech Companies Today. Learn by doing it yourself from scratch... Build real projects henceforth! Emenwa Global instructors are industry experts with years of practical, real-world experience building software at industry leading companies. They are sharing everything they know to teach thousands of students around the world, just like you, the most in-demand technical and non-technical skills (which are commonly overlooked) in the most efficient way so that you can take control of your life and unlock endless exciting new career opportunities in the world of technology, no matter your background or experience. One other important philosophy is that our courses are taught by real professionals, software developers with real and substantial experience in the industry, who are also great teachers. All our instructors are experienced, software developers. Whether you are a beginner, looking to learn how to program for the very first time, or to brush up on your existing skills, or to learn new languages and frameworks, the Academy has you covered. | Computer Vision | Senior Role | >=4 | Below 1K | >=15K | >=4 | Below 10 K | >=1 Lakh | |||||||||||||||||
Calculus - Mathematics for Data Science - Machine Learning | Mastering Calculus - Mathematics for Deep learning / Machine learning / Data Science / Data Analysis / AI - Hands On | 4.4 | 149 | 2213 | Created by Manifold AI Learning ® | Nov-22 | English | $11.99 | 9h 55m total length | https://www.udemy.com/course/deep-learning-calculus-data-science-machine-learning-ai/ | Manifold AI Learning ® | Learn the Future - Data Science, Machine Learning & AI | 4.6 | 567 | 18684 | Do you want to be better data Scientist ? Are you looking for way to stand out in the crowd? Interested in increasing your Machine Learning, Deep Learning expertise by effectively applying the mathematical skills ? If the Answer is Yes. Then, this course is for you. Calculus for Deep learning "Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python " With this course, You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning, Deep Learning , Artificial intelligence, Data Science Application. Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background. Whether you are building Self driving cars, or building the recommendation engine for Netflix, or trying to fit the practice data for a function Your data, Will have some type of labelled input and , some type of labelled output. A typical goal would always be fit these data to the function by adjusting the parameters. Hence in our course, We start from understanding the basics of functions which you might have touched upon in highschool. And then, In further sections, we move along and apply the basics and learn some of the important concepts related to approximation which is the core for any Machine learning, Deep Learning , Artificial intelligence, Data Science model And, in the last two sections of this course, We make use of all our learning from previous sections, and train our Neural networks and understand how we apply in Linear Regression models by writing the code from scratch. We are sure that you will be amazed how well you can perform in your work once you have the intuition of calculus. This course is carefully designed by experts with student’s feedback so that you can have the premium learning experience. Join now to build confidence in Mathematics part of Machine learning, Deep Learning , Artificial intelligence, Data Science and stay ahead in your career. See you in the Lesson 1. | https://www.udemy.com/course/deep-learning-calculus-data-science-machine-learning-ai/#instructor-1 | Manifold AI Learning ® is an online Academy with the goal to empower the students with the knowledge and skills that can be directly applied to solving the Real world problems in Data Science, Machine Learning and Artificial intelligence. Checkout our instructor profile for the complete list of courses. All the best for your Learning. - Team ManifoldAILearning ® "Learn the Future" | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
The Visual Guide on How Neural Networks Learn from Data | The BEST Resource for Understanding Neural Networks and How They Learn | 4.6 | 146 | 3204 | Created by Mauricio Maroto | Aug-20 | English | $10.99 | 2h 47m total length | https://www.udemy.com/course/the-visual-guide-on-how-neural-networks-learn-from-data/ | Mauricio Maroto | +10,000 students and growing! | 4.2 | 1547 | 11604 | Course Achievements (January 2021): +3,100 Worldwide Students enrolled Trophy Awards for Key Section Achievements! ═════════════════════════════════════════════════════ Some Student Reviews are: "This is an excellent course for people who wish to get an introductory experience in learning about Neural Networks." (December 2020). "This is a very good introduction on how ANN work. It helps build intuition both on the backpropagation and the math behind it." (January 2020). "This course does what it claims to do very well." (October 2019) "Very structured and logical" (July 2018). "Enlightening overview of how neural networks operate mathematically." (March 2018). "I just loved this course. The course is very well taught and is divided in easy-to-digest units." (December 2017). ═════════════════════════════════════════════════════ Hi. Thanks for showing interest in this course! What makes this course special: Step-by-Step Neural Network Learning Process, Master topics like Fundamentals, Objectives, Required Datasets, Weights, Biases, Nodes, Activation functions, Feed-Forward Passes, Predictions, Losses, Gradient Descent, Learning, Backpropagation and more! Plus, personalized feedback and help. You ask, I answer directly! This is your BEST resource for Neural Networks (NN) learning! A must for understanding special concepts and not get lost in computing your own NNs: ✅ First: You'll start the Neural Networks Primer with Fundamentals, Objectives, Data and more: Learn concepts using analogies for maximum learning, so you will be fully covered. Learning how NNs learn will be easy with this Primer under your sleeve! ✅ Second: You'll continue the NN Primer with Learning, Backpropagation and Predictions and more topics In an easy and intuitive way, you will understand how they work, This is fundamental in the NN Learning Process. At the end of this section, you will have mastered the NN Primer! Now, you are ready for the Step-by-Step (in-Motion) sections! ✅ Third: You'll start the in-Motion section with Inputs, Weights, Biases, Activations, Nodes and Feed-Forward Passes: See how they work inside an NN, Step-by-step templates, so you can follow every detail, These files will be dynamic, so you'll understand how NNs work as numbers will be updated on-the-fly and right in front of your eyes. ✅ Forth: You'll continue with the in-Motion section with NN Learning, Backpropagation, Tuning and Prediction: You will understand how NNs learn from the data. This all part of the dynamic templates you get to keep. You'll do several examples along the way for maximum learning. Lastly, you'll see what NNs do to make the best predictions. ✅ Fifth: You'll finish the in-Motion section by doing a complete rundown on everyting you've learned so far: You'll see how all NN inner components work for learning and prediction. Pay close attention at how all parts adjust, making the NN learn in front of your eyes. After this section, you will be fully versed on how NNs learn! ✅ Sixth: I will devote a section for more additional knowledge and resources for continous learning. And then, I will conclude with some Final Words. What are the Requirements? The only thing you'll need for this course is: Excel and PowerPoint: It is that easy! You will also need to bring your Basic Maths too, If you bring your Calculus (Derivatives) knowledge, that will be a big plus for you (but not required), What are some of the Benefits? As it is usual in my courses, you will get all files and spreadsheets for all lectures. This way you can replicate everything I do immediately after each lecture. Neural Networks are the new thing today. With it, you can explore and engage Artificial Intelligence, which I recommend you to dive in as it's part of the future. Plus, it's very rewarding and fun too! New content coming in the near future, let me know yout thoughts. Lastly, you can post questions or doubts, and I’ll answer to you personally. I hope you find this course as useful as I have creating it! I’ll see you inside, -M.A. Mauricio M. | https://www.udemy.com/course/the-visual-guide-on-how-neural-networks-learn-from-data/#instructor-1 | Mauricio Maroto holds a Master degree in Industrial Economics from Carlos III University (Madrid, Spain). With more than 8 years of professional experience, he's always been involved in creating insights for strategic decision making through data analysis. From data query, cleaning and processing, to clustering, regression and classification techniques, Mauricio is passionate about finding patterns and trends in large datasets. Mauricio's other passions are his family, friends, his pets and he loves to play soccer, whether indoors or outdoors. He also goes out for a run on weekends (not lately). He also loves topics such as Economics, Programming and Science. | Neural Networks | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
No-Code Machine Learning Using Amazon AWS SageMaker Canvas | Build your Machine Learning Model and get accurate predictions without writing any Code using AWS SageMaker Canvas | 4.3 | 145 | 30090 | Created by Prince Patni | Dec-21 | English | $9.99 | 1h 20m total length | https://www.udemy.com/course/no-code-machine-learning-using-amazon-aws-sagemaker-canvas/ | Prince Patni | Software Developer (BI, Data Science) | 4.2 | 3230 | 188924 | This AWS SageMaker Canvas Course will help you to become a Machine Learning Expert and will enhance your skills by offering you comprehensive knowledge, and the required hands-on experience on this newly launched Cloud based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise. Top Reasons why you should learn AWS SageMaker Canvas : AWS is the #1 cloud based tool used industry wide for Machine Learning Projects. You do not need Advanced Coding expertise generally required in the field of Machine Learning. Complex knowledge of Statistics, Algorithms, Mathematics that is difficult to master is also not required. Machine Learning Models that usually takes many days to build, are available very quickly in just a few minutes. The demand for ML professionals is on the rise. This is one of the most sought-after profession currently in the lines of Data Science. There are multiple opportunities across the Globe for everyone with Machine Learning skills. SageMaker Canvas has a small learning curve and you can pick up even advanced concepts very quickly. This Tool is available as a part of AWS Free Tier. You do not need high configuration computer to learn this tool. All you need is any system with internet connectivity. Top Reasons why you should choose this Course : This course is designed keeping in mind the students from all backgrounds - hence we cover everything from basics, and gradually progress towards advanced topics. We take live Industry Projects and do each and every step from start to end in the course itself. This course can be completed in a Day ! All Doubts will be answered. Most Importantly, Guidance is offered beyond the Tool - You will not only learn the Software, but important Machine Learning principles. Also, I will share the resources where to get the best possible help from, & also the sources to get public datasets to work on to get mastery in the ML domain. A Verifiable Certificate of Completion is presented to all students who undertake this AWS SageMaker Canvas course. | https://www.udemy.com/course/no-code-machine-learning-using-amazon-aws-sagemaker-canvas/#instructor-1 | Studied Engineering, worked in 4 MNCs by far, and traveled across the Globe for work. Currently in the role of Analyst/Developer in a reputed Organization. Loves teaching, loves learning. Here to share Data Analytics, Data Visualization, Business Intelligence, Data Science and other Software Development tips. Motto of life : Sky above me. Earth below me. Fire within me. | Machine Learning | Engineer/Developer | >=4 | Below 1K | >=30K | >=4 | Below 10 K | >=1.5 Lakh | |||||||||||||||||
Practical Data Science: Reducing High Dimensional Data in R | In this R course, we'll see how PCA can reduce a 5000+ variable data set into 10 variables and barely lose accuracy! | 4.3 | 144 | 1793 | Created by Manuel Amunategui | Apr-17 | English | $19.99 | 2h 24m total length | https://www.udemy.com/course/practical-data-science-reducing-high-dimensional-data-in-r/ | Manuel Amunategui | Data Scientist & Quantitative Developer | 4.5 | 1487 | 47993 | In this R course, we'll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We'll look at different ways of measuring PCA's effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We'll also look at the advantages and disadvantages with different ways of reducing data. | https://www.udemy.com/course/practical-data-science-reducing-high-dimensional-data-in-r/#instructor-1 | Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, author of Monetizing Machine Learning and The Little Book of Fundamental Indicators, founder of FastML, reached top 1% on Kaggle and awarded "Competitions Expert" title, taught over 20,000 students on Udemy and VP of Data Science at SpringML. From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say: "It just ain’t real 'til it reaches your customer’s plate" I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied to machine learning. Reach me at [email protected] | Misc | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | |||||||||||||||||
How to easily use ANN for prediction mapping using GIS data? | First Simplified Step-by-Step Artificial Neural Network Methodology in R for Prediction Mapping using GIS Data | 4.3 | 144 | 690 | Created by Dr. Omar AlThuwaynee | Feb-22 | English | $9.99 | 7h 16m total length | https://www.udemy.com/course/how-to-use-ann-for-prediction-mapping-using-gis-data/ | Dr. Omar AlThuwaynee | PhD. of Civil and Geomatics Engineering | 4.2 | 532 | 2074 | Artificial Neural Network (ANN) is one of the advanced Artificial Intelligence (AI) component, through many applications, vary from social, medical and applied engineering, ANN proves high reliability and validity enhanced by multiple setting options. Using ANN with Spatial data, increases the confidence in the obtained results, especially when it compare to regression or classification based techniques. as called by many researchers and academician especially in prediction mapping applications. Together, step by step with "school-bus" speed, will cover the following points comprehensively (data, code and other materials are provided) using NeuralNet Package in R and Landslides data and thematics maps. Produce training and testing data using automated tools in QGIS OR SKIP THIS STEP AND USE YOUR OWN TRAINING AND TESTING DATA Run Neural net function with training data and testing data Plot NN function network Pairwise NN model results of Explanatories and Response Data Generalized Weights plot of Explanatories and Response Data Variables importance using NNET Package function Run NNET function Plot NNET function network Variables importance using NNET Sensitivity analysis of Explanatories and Response Data Run Neural net function for prediction with validation data Prediction Validation results with AUC value and ROC plot Produce prediction map using Raster data Import and process thematic maps like, resampling, stacking, categorical to numeric conversion. Run the compute (prediction function) Export final prediction map as raster.tif | https://www.udemy.com/course/how-to-use-ann-for-prediction-mapping-using-gis-data/#instructor-1 | Omar AlThuwaynee, is a Postdoctoral researcher at Research Institute for Geo-Hydrological Protection IRPI, Italian National Research Council, Rome, Italy. And, is the CEO of Scientists Adoption Academy "A Free Research Collaboration" website. Carry a BEng. and MSc. in Civil Engineering and the Built environment, PhD. in GIS and Geomatics Engineering. And editor in Landslides (Journal of the International Consortium on Landslides). Specialist in natural hazards, geospatial data analysis, Data Mining and GIS applications, with more than 10 academic years of experience. My published record of research articles in peer reviewed journals, focus mainly on: Urban infrastructure projects, Natural and man-made hazards analysis and Risk management, and Spatial data analysis. Welcome to my research groups on Scientists Adoption Academy (scadacademy). Geomatics for Better Life..! | Misc | Engineer/Developer | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Intelligently Extract Text & Data from Document with OCR NER | Develop Document Scanner App project that is Named entity extraction from scan documents with OpenCV, Pytesseract, Spacy | Bestseller | 4.5 | 143 | 1142 | Created by Gusksra R, Data Science Anywhere | Nov-22 | English | $9.99 | 7h 17m total length | https://www.udemy.com/course/business-card-reader-app/ | Gusksra R | Instructor | 4.4 | 1019 | 80343 | Welcome to Course "Intelligently Extract Text & Data from Document with OCR NER" !!! In this course you will learn how to develop customized Named Entity Recognizer. The main idea of this course is to extract entities from the scanned documents like invoice, Business Card, Shipping Bill, Bill of Lading documents etc. However, for the sake of data privacy we restricted our views to Business Card. But you can use the framework explained to all kinds of financial documents. Below given is the curriculum we are following to develop the project. To develop this project we will use two main technologies in data science are, Computer Vision Natural Language Processing In Computer Vision module, we will scan the document, identify the location of text and finally extract text from the image. Then in Natural language processing, we will extract the entitles from the text and do necessary text cleaning and parse the entities form the text. Python Libraries used in Computer Vision Module. OpenCV Numpy Pytesseract Python Libraries used in Natural Language Processing Spacy Pandas Regular Expression String As are combining two major technologies to develop the project, for the sake of easy to understand we divide the course into several stage of development. Stage -1: We will setup the project by doing the necessary installations and requirements. Install Python Install Dependencies Stage -2: We will do data preparation. That is we will extract text from images using Pytesseract and also do necessary cleaning. Gather Images Overview on Pytesseract Extract Text from all Image Clean and Prepare text Stage -3: We will see how to label NER data using BIO tagging. Manually Labeling with BIO technique B - Beginning I - Inside O - Outside Stage -4: We will further clean the text and preprocess the data for to train machine learning. Prepare Training Data for Spacy Convert data into spacy format Stage -5: With the preprocess data we will train the Named Entity model. Configuring NER Model Train the model Stage -6: We will predict the entitles using NER and model and create data pipeline for parsing text. Load Model Render and Serve with Displacy Draw Bounding Box on Image Parse Entitles from Text Finally, we will put all together and create document scanner app. Are you ready !!! Let start developing the Artificial Intelligence project. | https://www.udemy.com/course/business-card-reader-app/#instructor-1 | I am Gusksra working in Data Science with a demonstrated history of working in the information technology and services industry. Skilled in Machine Learning, Deep Learning, Statistical algorithms. We mostly worked on Image Processing and Natural Language processing application. I also successfully deployed many data science-related projects in cloud platforms as a service in AWS, Google Cloud, etc. | Misc | Teacher/Trainer/Professor/Instructor | >=4 | Below 1K | Below 10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||
Data Science:Hands-on Covid-19 Data Analysis & Visualization | Create 45 graphs including Choropleth maps, WordCloud, Animation, Bar graphs, scatter plots & more to visualize Covid-19 | 4 | 143 | 13781 | Created by School of Disruptive Innovation | Sep-20 | English | $9.99 | 2h 5m total length | https://www.udemy.com/course/hands-on-covid-19-data-visualization-using-plotly-express/ | School of Disruptive Innovation | Creative Learning Solutions for the Digital Age | 4.1 | 1049 | 42339 | Have you always wanted to create amazing graphs and charts to present your ideas but did not know where to start? Would you like to Visualize Covid-19 using bar graphs, bubble graphs, WordCloud and Animations? Have you ever wanted to create graphs and charts that would bring your ideas to life and make your audience go “WOW”? If the answer to any of the questions is “YES”, then this is your course on data visualization in Python using Plotly express: A hands-on, practical and comprehensive course on Data Visualization using Plotly express. Create Amazing, Excellent quality, publication-ready graph with just one line of Code. Yes, you heard it right. With just a single line of code. 1 Graph= 1 Line of Code Do you know what the best part is? You don’t need to be a programming expert to do it. You just need a very basic understanding of Python and that would be more than enough to create amazing publication-ready graphs. This is a Practical Hands-on Course hence for the best learning experience we recommend you to type the codes in your own notebook following the lessons carefully. No Unnecessary lectures. No unnecessary details. In the next 2 hours, learn to create 45 different publication-ready graphs and charts that will “WOW” anyone who sees them.. But that’s not all: The same data visualization skills can be used for many other purposes like : Sales Data Visualization Office reports Visualization Any other kind of Visualization You can Visualize absolutely any kind of data. We will complete the following tasks in this hand-on project : Task 1: Importing Libraries Task 2: Importing Datasets Task 3: Data Cleaning Task 4: Bar graphs- Comparisons between COVID infected countries in terms of total cases, total deaths, total recovered & total tests Task 5: Data Visualization through Bubble Charts-Continent Wise Task 6: Data Visualization through Bubble Charts-Country Wise Task 7: Visualizing relationship between Total cases, Total deaths and Total tests Task 8: Advanced Data Visualization- Bar graphs for All top infected Countries Task 9: Advanced Data Visualization- Countries Specific COVID Data Visualization: United States Task 10: Advanced Data Visualization- Countries Specific COVID Data Visualization: India Task 11: Geographical Data Visualization - Choropleth maps Animation- Equi-rectangular projection Task 12: Geographical Data Visualization - Choropleth maps Animation- Orthographic and Natural Earth projection Task 13: Bar animation- Cases growth through Continent Task 14: Text Visualization using WordCloud- Specific reasons for COVID related deaths Task 15: Text Visualization using WordCloud- Generic reasons for COVID related deaths We will be using Google Colab as our notebook. This course is a Hands-on guided project which essentially means, you will be creating these 45 amazing publication-ready graphs on your notebook alongside the lessons. I write the code and then you write the code. At the end of this course, you will have created 45 graphs all by yourself: One simple line of code for one amazing graph. Data Visualization is the most demanded skill of the 21st century. And This skill can be yours just for the price of lunch. You will receive : Certificate of completion from the School of Disruptive Education All the datasets in the resources section of the respective lecture Link to the Google Colab notebook which has all the codes in it. So what are you waiting for? Grab a coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. Happy Learning | https://www.udemy.com/course/hands-on-covid-19-data-visualization-using-plotly-express/#instructor-1 | Welcome to the School of the Disruptive Innovation. We are here to teach you what they don't teach you in school. We are unconventional in our ways but we promise and we over-deliver. We have a community of over 40,000+ students and 60,000+ enrollments across 166 countries. We offer courses on Data Science (Classical machine Learning, Deep learning, BigData, Data Visualization & Analysis), Android Development, Web Development, and Graphics Design. Every course is created and delivered by professionals in the field such as Technology related courses by software engineers and business related courses are created by business experts. | Misc | >=4 | Below 1K | >=10K | >=4 | Below 10 K | Below 1 Lakh | ||||||||||||||||||
Learn Basic Data science and Python Libraries | Covers all Essential Python topics and Libraries for Data Science or Machine Learning Beginner Such as numpy pandas etc. | 3.2 | 143 | 36012 | Created by Akbar Khan | Nov-20 | English | $9.99 | 2h 30m total length | https://www.udemy.com/course/basics-data-science-with-numpy-pandas-and-matplotlib/ | Akbar Khan | Engineer and Online Instructure | 3.2 | 143 | 36012 | Welcome to my course Basics Data Science with Numpy, Pandas, and Matplotlib This course will teach the basics of Python Data Structures and the most important Data Science libraries like NumPy and Pandas with step-by-step examples! In this course, we will learn step by step with starting with basics understanding of jupyter notebook and how to write a code in jupyter notebook and understanding every function of jupyter notebook then we will learn basic pythons such as Then we will go ahead with the basic python data types like strings, numbers, and its operations. We will deal with different types of ways to assign and access strings, string slicing, replacement, concatenation, formatting, and strings. Dealing with numbers, we will discuss the assignment, accessing, and different operations with integers and floats. The operations include basic ones and also advanced ones like exponents. Also, we will check the order of operations, increments, and decrements, rounding values, and typecasting. Then we will proceed with basic data structures in python like Lists tuples and set. For lists, we will try different assignments, access, and slicing options. Along with popular list methods, we will also see list extension, removal, reversing, sorting, min and max, existence check, list looping, slicing, and also inter-conversion of lists and strings. So let's start with the lessons. See you soon in the course lecture | https://www.udemy.com/course/basics-data-science-with-numpy-pandas-and-matplotlib/#instructor-1 | I Have completed my Bachelor of Engineering(B.E) From Mumbai University In electronic and telecommunication department, I am also writing blogs on new technology in data science and machine learning and deep learning I have completed my data science course on Coursera data science program and I have also done many certifications on data science and machine learning from udemy and many online platforms I am also pursuing a data science internship in a different company I have also published a paper cancer detection using machine learning and deep learning Currently, I am a Studying a Data Science and Machine Learning, DeepLearning, I am also an online instructor | Python | Engineer/Developer | >=3 | Below 1K | >=35K | >=3 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Machine Learning: Beginner Reinforcement Learning in Python | How to teach a neural network to play a game using delayed gratification in 146 lines of Python code | 4.6 | 143 | 470 | Created by Milo Spencer-Harper | Jan-20 | English | $9.99 | 1h 44m total length | https://www.udemy.com/course/machine-learning-beginner-reinforcement-learning-in-python/ | Milo Spencer-Harper | Software Engineer | 4.5 | 348 | 1117 | This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification. We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward. Using this example I will teach you Deep Q Learning - a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari. | https://www.udemy.com/course/machine-learning-beginner-reinforcement-learning-in-python/#instructor-1 | After studying at Oxford University, I was struck by how the best professors made very complex ideas easy to understand. That is my mission. I believe in bringing the breakthroughs occurring in artificial intelligence from inaccessible academic papers to anyone who wants to learn. Over 600,000 students have read my blog post "How to build a neural network in 9 lines of Python code". I'm excited to bring courses on machine learning and artificial intelligence to Udemy. | Machine Learning | Engineer/Developer | >=4 | Below 1K | Below 1K | >=4 | Below 1 K | Below 10 K | |||||||||||||||||
Apache Spark with Scala By Example | Advance your Spark skills and become more valuable, confident, and productive | 3.6 | 143 | 1415 | Created by Todd McGrath | May-16 | English | $9.99 | 2h 48m total length | https://www.udemy.com/course/learning-spark/ | Todd McGrath | Data Engineer, Software Developer, Mentor | 3.6 | 312 | 2294 | Understanding how to manipulate, deploy and leverage Apache Spark is quickly becoming essential for data engineers, architects, and data scientists. So, it's time for you to stay ahead of the crowd by learning Spark with Scala from an industry veteran and nice guy. This course is designed to give you the core principles needed to understand Apache Spark and build your confidence through hands-on experiences. In this course, you’ll be guided through a wide range of core Apache Spark concepts using Scala source code examples; all of which are designed to give you fundamental, working knowledge. Each section carefully builds upon previous sections, so your learning is reinforced along every step of the way. All of the source code is conveniently available for download, so you can run and modify for yourself. Here are just a few of concepts this course will teach you using more than 50 hands-on examples: Learn the fundamentals and run examples of Spark's Resilient Distributed Datasets, Actions and Transformations through Scala Run Spark on your local cluster and also Amazon EC2 Troubleshooting tricks when deploying Scala applications to Spark clusters Explore Spark SQL with CSV, JSON and mySQL database (JDBC) data sourcesDiscover Spark Streaming through numerous examples and build a custom application which streams from SlackHands-on machine learning experiments with Spark MLlib Reinforce your understanding through multiple quizzes and lecture recap Check out the free preview videos below!As an added bonus, this course will teach you about Scala and the Scala ecosystem such as SBT and SBT plugins to make packaging and deploying to Spark easier and more efficient. As another added bonus, on top of all the extensive course content, the course offers a private message board so you can ask the instructor questions at anytime during your Spark learning journey. This course will make you more knowledgeable about Apache Spark. It offers you the chance to build your confidence, productivity and value in your Spark adventures. | https://www.udemy.com/course/learning-spark/#instructor-1 | Todd has an extensive and proven track record in software development leadership and building solutions for the world's largest brands and Silicon Valley startups. His courses are taught using the same skills used in his consulting and mentoring projects. Todd believes the only way to gain confidence and become productive is to be hands-on through examples. Each new subject should build upon previous examples or presentation, so each step is also a way to reemphasis a prior topic. To learn more about Todd, visit his LinkedIn profile. | Scala | Engineer/Developer | >=3 | Below 1K | Below 10K | >=3 | Below 1 K | Below 10 K | |||||||||||||||||
Unleash Machine Learning: Build Artificial Neuron in Python | A journey into Machine Learning concepts using your very own Artificial Neural Network: Load, Train, Predict, Evaluate | 4.4 | 142 | 1982 | Created by Razvan Pistolea | Oct-17 | English | $9.99 | 3h 0m total length | https://www.udemy.com/course/unleash-machine-learning-build-artificial-neuron-in-python/ | Razvan Pistolea | Source Code Painter | 4.3 | 246 | 10276 | Cars that drive themselves hundreds of miles with no accidents? Algorithms that recognize objects and faces from images with better performance than humans? All possible thanks to Machine Learning! In this course you will begin Machine Learning by implementing and using your own Artificial Neuronal Network for beginners. In this Artificial Neuronal Network course you will: understand intuitively and mathematically the fundamentals of ANN implement from scratch a multi layer neuronal network in Python load and visually explore different datasets transform the data train you network and use it to make predictions measure the accuracy of your predictions use machine learning tools and techniques Jump in directly: All sourcecode and notebooks on public GitHub Apply Machine Learning: section 4 Implement the ANN: section 3 Full ride: section 1, 2, 3, 4 | https://www.udemy.com/course/unleash-machine-learning-build-artificial-neuron-in-python/#instructor-1 | I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too. | Machine Learning | >=4 | Below 1K | Below 10K | >=4 | Below 1 K | Below 1 Lakh | ||||||||||||||||||
Artificial Intelligence IV - Reinforcement Learning in Java | All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach | 4.6 | 142 | 1685 | Created by Holczer Balazs | Dec-21 | English | $9.99 | 3h 2m total length | https://www.udemy.com/course/artificial-intelligence-iv-reinforcement-learning-in-java/ | Holczer Balazs | Software Engineer | 4.5 | 32417 | 252739 | This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics: Markov Decision Processes value-iteration and policy-iteration Q-learning fundamentals pathfinding algorithms with Q-learning Q-learning with neural networks | https://www.udemy.com/course/artificial-intelligence-iv-reinforcement-learning-in-java/#instructor-1 | My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Take a look at my website if you are interested in these topics! | Artificial Intelligence | Engineer/Developer | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | >=2.5 Lakh | |||||||||||||||||
Object Oriented Programming in Python - Aided with Diagrams | Concept Building, Syntax and Examples of Object Oriented Programming (OOP) in Python including Inheritance | 3.5 | 142 | 21343 | Created by Kumail Raza, Frahaan Hussain | Jun-19 | English | $9.99 | 43m total length | https://www.udemy.com/course/object-oriented-programming-in-python/ | Kumail Raza | BI Developer | Instructor | Facilitator | 4 | 473 | 68544 | Object-oriented programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data and code: data in the form of fields (often known as attributes or properties), and code, in the form of procedures (often known as methods). A feature of objects is that an object's own procedures can access and often modify the data fields of itself (objects have a notion of this or self). In OOP, computer programs are designed by making them out of objects that interact with one another. OOP languages are diverse, but the most popular ones are class-based, meaning that objects are instances of classes, which also determine their types. Many of the most widely used programming languages (such as C++, Java, Python, etc.) are multi-paradigm and they support object-oriented programming to a greater or lesser degree, typically in combination with imperative, procedural programming. This course includes; -Class, -Objects, -Inheritance (Multi-level and Multi-layers of Inheritance) -Overriding the functionality of Parent Class -Method Resolution Order -Operator Overloading with concepts, diagrams, syntax and examples and Some of the Common Operator Overloading Special Functions in Python # Operator Expression Internally # Addition p1 + p2 p1.__add__(p2) # Subtraction p1 - p2 p1.__sub__(p2) # Multiplication p1 * p2 p1.__mul__(p2) # Power p1 ** p2 p1.__pow__(p2) # Division p1 / p2 p1.__truediv__(p2) | https://www.udemy.com/course/object-oriented-programming-in-python/#instructor-1 | I am a BI Developer | Instructor | Facilitator graduate in Business Finance and Computer Science. Certified in Statistics, Data Analytics, Machine Learning, and Visualization. I have been supporting the data-driven decision, consulting, creating, implementing and automating Data Intelligence and Analytics Strategies while identifying KPIs in the process and how it affects the overall business strategy. Blogger on Microsoft Power BI Community Hands-on experience (Intermediate to advanced level) in using the following tools; Business Intelligence | Analytics: Microsoft Azure Machine Learning Studio, Microsoft Power BI, Mode Analytics, Microsoft Excel / Google Spreadsheet, Python (Pandas, Openpyxl, matplotlib) Database: SQL Server 2016 (ETL into Power BI), MYSQL Project Management: Wrike, GetFlow, Jira. Documentation: Confluence, Dropbox Paper, Dropbox. Adobe Acrobat PDF, Microsoft Word, and Microsoft Excel. Diagram | Wireframe | Mockups: Balsamiq Mockups, Drawdotio, Microsoft Visio, Microsoft Excel, and Word Smart Art Graphic. Analyzed CRM Applications: ZOHO CRM, Rethink Residential and Commercial (Built on Salesforce Platform), Podio, Plan plus, ConvergeEnterprise, dotloop, Microsoft Dynamics NAV. | Python | Engineer/Developer | >=3 | Below 1K | >=20K | >=4 | Below 1 K | Below 1 Lakh | |||||||||||||||||
Machine Learning and Deep Learning using Tensor Flow & Keras | A-Z Course for Google's Deep Learning Framework - TensorFlow with Python! Learn to use functions and apply Codes. | 2.5 | 142 | 1929 | Created by Dr Aashish Dikshit, PHD(Founder of Lakshmish academy) | Jun-18 | English | $9.99 | 11h 3m total length | https://www.udemy.com/course/dlmltensorflow/ | Dr Aashish Dikshit, PHD(Founder of Lakshmish academy) | An ex-Cisco, GE, HP& JP Morgan Chase.(Data scientist) | 2.8 | 455 | 4565 | This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This is a comprehensive course with very crisp and straight forward intent. This course covers a variety of topics, including Neural Network BasicsTensorFlow detailed,Keras,Sonnet etcArtificial Neural NetworksTypes of Neural networkFeed forward networkRadial basis networkKohonen Self organizing mapsRecurrent neural NetworkModular Neural networksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksMachine Learning Deep Learning Framework comparisons There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, IBM, Intel, and of course, Google! Become a machine learning guru today! We'll see you inside the course! | https://www.udemy.com/course/dlmltensorflow/#instructor-1 | Lakshmish academy(Formerly Indira Academy) was found in 2001 with the belief of providing high quality ,in depth courses on Radio diagnosis & Technology, available at affordable price. We strive to serve & change lives by our teaching. We are bunch of Instructors who are Super specialized & Doctorate(PHD) in Computer Science & Data Sciences .We have experience in teaching our subjects for over two decade now .We have helped hundreds of people become champion in the subjects and enabled them to change their lives. Our graduates work at companies like Google, Cisco, and Facebook. The whole effort is for the betterment of students and knowledge sharing. We have been hired to impart the best technical training's by the companies like GE, Cisco, HP,J P Morgan Chase and Flipkart. We have now focused our time on bringing our classroom teaching experience to an online environment. Join us in this amazing adventure! | Machine Learning | Data Scientist | >=2 | Below 1K | Below 10K | >=2 | Below 1 K | Below 10 K | |||||||||||||||||
The Fun and Easy Guide to Machine Learning using Keras | Learn 16 Machine Learning Algorithms in a Fun and Easy along with Practical Python Labs using Keras | 3.9 | 141 | 1851 | Created by Augmented Startups, Minerva Singh | Jan-19 | English | $10.99 | 5h 10m total length | https://www.udemy.com/course/machine-learning-fun-and-easy-using-python-and-keras/ | Augmented Startups | M(Eng) AI Instructor 97k+ Subs on YouTube & 60k+ students | 3.8 | 3532 | 56203 | Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science. So Many Machine Learning Courses Out There, Why This One? This is a valid question and the answer is simple. This is the ONLY course on Udemy which will get you implementing some of the most common machine learning algorithms on real data in Python. Plus, you will gain exposure to neural networks (using the H2o framework) and some of the most common deep learning algorithms with the Keras package. We designed this course for anyone who wants to learn the state of the art in Machine learning in a simple and fun way without learning complex math or boring explanations. Each theoretically lecture is uniquely designed using whiteboard animations which can maximize engagement in the lectures and improves knowledge retention. This ensures that you absorb more content than you would traditionally would watching other theoretical videos and or books on this subject. What you will Learn in this Course This is how the course is structured: Regression – Linear Regression, Decision Trees, Random Forest Regression, Classification – Logistic Regression, K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes, Clustering - K-Means, Hierarchical Clustering, Association Rule Learning - Apriori, Eclat, Dimensionality Reduction - Principle Component Analysis, Linear Discriminant Analysis, Neural Networks - Artificial Neural Networks, Convolution Neural Networks, Recurrent Neural Networks. Practical Lab Structure You DO NOT need any prior Python or Statistics/Machine Learning Knowledge to get Started. The course will start by introducing students to one of the most fundamental statistical data analysis models and its practical implementation in Python- ordinary least squares (OLS) regression. Subsequently some of the most common machine learning regression and classification techniques such as random forests, decision trees and linear discriminant analysis will be covered. In addition to providing a theoretical foundation for these, hands-on practical labs will demonstrate how to implement these in Python. Students will also be introduced to the practical applications of common data mining techniques in Python and gain proficiency in using a powerful Python based framework for machine learning which is Anaconda (Python Distribution). Finally you will get a solid grounding in both Artificial Neural Networks (ANN) and the Keras package for implementing deep learning algorithms such as the Convolution Neural Network (CNN). Deep Learning is an in-demand topic and a knowledge of this will make you more attractive to employers. Excited Yet? So as you can see you are going to be learning to build a lot of impressive Machine Learning apps in this 3 hour course. The underlying motivation for the course is to ensure you can apply Python based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and IMPRESS your potential employers with an actual examples of your machine learning abilities. It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. TAKE ACTION TODAY! We will personally support you and ensure your experience with this course is a success. And for any reason you are unhappy with this course, Udemy has a 30 day Money Back Refund Policy, So no questions asked, no quibble and no Risk to you. You got nothing to lose. Click that enroll button and we'll see you in side the course. | https://www.udemy.com/course/machine-learning-fun-and-easy-using-python-and-keras/#instructor-1 | So a bit about me, Ritesh Kanjee: I've graduated from University of Johannesburg as an Electronic Engineer with a Masters in Image Processing and 8 years ago I started my online school called Augmented Startups where I have over 97'000 subscribers on YouTube and over 60'000 students on Augmented AI Bootcamp/Udemy. I’ve worked with popular tools such as TensorFlow Keras, Open CV, and PyTorch and I’ve also produced High ranking tutorials that feature on Google and YouTube. My Machine Learning Series is also one of the most viewed videos, over 300 thousand views and you’ll find them ranked right at the top on YouTube search results. From my tutorials, I have received a lot of great feedback and testimonials from students all around the world, I will share those reviews towards the end of the video And I have also presented at international conferences and meetups in AI. For industry standard AI, I have partnered up with Geeky Bee AI who are Experts in the field in AI and Deep Learning and have experience developing AI apps for real world applications. | Machine Learning | Teacher/Trainer/Professor/Instructor | >=3 | Below 1K | Below 10K | >=3 | Below 10 K | Below 1 Lakh | |||||||||||||||||
Complete Deep Learning In R With Keras & Others | Deep Learning: Master Powerful Deep Learning Tools in R Like Keras, Mxnet, H2O and Others | 4.7 | 141 | 1298 | Created by Minerva Singh | Dec-19 | English | $9.99 | 7h 55m total length | https://www.udemy.com/course/complete-deep-learning-in-r-with-keras-others/ | Minerva Singh | Bestselling Instructor & Data Scientist(Cambridge Uni) | 4.4 | 17568 | 89129 | YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level! LEARN FROM AN EXPERT DATA SCIENTIST: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R. Among other things: You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods. You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras framework You will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications. With this course, you’ll have the keys to the entire R Neural Networks and Deep Learning Kingdom! NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life. After taking this course, you’ll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model You’ll even understand the underlying concepts to understand what algorithms and methods are best suited for your data. We will also work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW! | https://www.udemy.com/course/complete-deep-learning-in-r-with-keras-others/#instructor-1 | I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). | Deep Learning | Data Scientist | >=4 | Below 1K | Below 10K | >=4 | Below 1 Lakh | Below 1 Lakh | |||||||||||||||||
Machine Learning A-Z™: Python & R in Data Science [2022] | Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included. | 4.5 | 164055 | 909006 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Dec-22 | English | $12.99 | 42h 32m total length | https://www.udemy.com/course/machinelearning/ | Kirill Eremenko | Data Scientist | 4.5 | 600 | 2 | Interested in the field of Machine Learning? Then this course is for you! This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way. Over 900,000 students world-wide trust this course. We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course can be completed by either doing either the Python tutorials, or R tutorials, or both - Python & R. Pick the programming language that you need for your career. This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way: Part 1 - Data Preprocessing Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 - Clustering: K-Means, Hierarchical Clustering Part 5 - Association Rule Learning: Apriori, Eclat Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now. Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects. | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Machine Learning | Data Scientist | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Python for Data Science and Machine Learning Bootcamp | Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more! | Bestseller | 4.6 | 124097 | 600758 | Created by Jose Portilla | May-20 | English | $12.99 | 24h 54m total length | https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 981 | 3 | Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with PythonNumPy with PythonUsing pandas Data Frames to solve complex tasksUse pandas to handle Excel FilesWeb scraping with pythonConnect Python to SQLUse matplotlib and seaborn for data visualizationsUse plotly for interactive visualizationsMachine Learning with SciKit Learn, including:Linear RegressionK Nearest NeighborsK Means ClusteringDecision TreesRandom ForestsNatural Language ProcessingNeural Nets and Deep LearningSupport Vector Machinesand much, much more! Enroll in the course and become a data scientist today! | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
The Data Science Course 2022: Complete Data Science Bootcamp | Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning | Bestseller | 4.6 | 116327 | 552072 | Created by 365 Careers, 365 Careers Team | Oct-22 | English | https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 622 | 2 | The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding of the data science field and the type of analysis carried out Mathematics Statistics Python Applying advanced statistical techniques in Python Data Visualization Machine Learning Deep Learning Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is. So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2022. We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). The Skills 1. Intro to Data and Data Science Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science. 2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on. Why learn it? Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. 3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist. Why learn it? This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. 4. Python Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning. Why learn it? When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language. 5. Tableau Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers. 6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail. Why learn it? Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section. 7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow. Why learn it? Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines. ***What you get*** A $1250 data science training program Active Q&A support All the knowledge to get hired as a data scientist A community of data science learners A certificate of completion Access to future updates Solve real-life business cases that will get you the job You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and become a part of our data scientist program today. | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Misc | >=4 | >=1 Lakh | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||||
R Programming A-Z™: R For Data Science With Real Exercises! | Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2 | Bestseller | 4.6 | 48651 | 245366 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Dec-22 | English | $12.99 | 10h 35m total length | https://www.udemy.com/course/r-programming/ | Kirill Eremenko | Data Scientist | 4.5 | 600 | 2 | Learn R Programming by doing! There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, What you will learn: Learn how to use R Studio Learn the core principles of programming Learn how to create vectors in R Learn how to create variables Learn about integer, double, logical, character, and other types in R Learn how to create a while() loop and a for() loop in R Learn how to build and use matrices in R Learn the matrix() function, learn rbind() and cbind() Learn how to install packages in R Sincerely, Kirill Eremenko | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | >=45K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
Deep Learning A-Z™: Hands-On Artificial Neural Networks | Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. | Bestseller | 4.5 | 41612 | 345211 | Created by Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team | Dec-22 | English | $12.99 | https://www.udemy.com/course/deeplearning/ | Kirill Eremenko | Data Scientist | 4.5 | 600 | 2 | *** As seen on Kickstarter ***Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence. --- Why Deep Learning A-Z? --- Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there: 1. ROBUST STRUCTURE The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it. That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning. 2. INTUITION TUTORIALS So many courses and books just bombard you with the theory, and math, and coding... But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms. With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer. 3. EXCITING PROJECTS Are you tired of courses based on over-used, outdated data sets? Yes? Well then you're in for a treat. Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges: Artificial Neural Networks to solve a Customer Churn problem Convolutional Neural Networks for Image Recognition Recurrent Neural Networks to predict Stock Prices Self-Organizing Maps to investigate Fraud Boltzmann Machines to create a Recomender System Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize *Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth. 4. HANDS-ON CODING In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after. This is a course which naturally extends into your career. 5. IN-COURSE SUPPORT Have you ever taken a course or read a book where you have questions but cannot reach the author? Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help. In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum. No matter how complex your query, we will be there. The bottom line is we want you to succeed. --- The Tools --- Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both! TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more. PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook. So which is better and for what? Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances. The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques. --- More Tools --- Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it. Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing. --- Even More Tools --- Scikit-learn the most practical Machine Learning library. We will mainly use it: to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation to improve our models with effective Parameter Tuning to preprocess our data, so that our models can learn in the best conditions And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience. Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently. --- Who Is This Course For? --- As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology. If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident. If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications. --- Real-World Case Studies --- Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges: #1 Churn Modelling Problem In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank. Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach. If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn. #2 Image Recognition In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder. For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog! #3 Stock Price Prediction In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans. The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course! In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them. #4 Fraud Detection According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course. This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card. This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications. #5 & 6 Recommender Systems From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models. Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of. And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix! --- Summary --- In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies. We are super enthusiastic about Deep Learning and hope to see you inside the class! Kirill & Hadelin | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Deep Learning | Data Scientist | >=4 | >=40K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Statistics for Data Science and Business Analysis | Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis | Bestseller | 4.6 | 35144 | 161265 | Created by 365 Careers, 365 Careers Team | Jan-21 | English | $12.99 | 4h 51m total length | https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/ | 365 Careers | Creating opportunities for Data Science and Finance students | 4.6 | 622 | 2 | Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? And you want to acquire the quantitative skills needed for the job? Well then, you’ve come to the right place! Statistics for Data Science and Business Analysis is here for you! (with TEMPLATES in Excel included) This is where you start. And it is the perfect beginning! In no time, you will acquire the fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. We have created a course that is: Easy to understand Comprehensive Practical To the point Packed with plenty of exercises and resources Data-driven Introduces you to the statistical scientific lingo Teaches you about data visualization Shows you the main pillars of quant research It is no secret that a lot of these topics have been explained online. Thousands of times. However, it is next to impossible to find a structured program that gives you an understanding of why certain statistical tests are being used so often. Modern software packages and programming languages are automating most of these activities, but this course gives you something more valuable – critical thinking abilities. Computers and programming languages are like ships at sea. They are fine vessels that will carry you to the desired destination, but it is up to you, the aspiring data scientist or BI analyst, to navigate and point them in the right direction. Teaching is our passion We worked full-time for several months to create the best possible Statistics course, which would deliver the most value to you. We want you to succeed, which is why the course aims to be as engaging as possible. High-quality animations, superb course materials, quiz questions, handouts and course notes, as well as a glossary with all new terms you will learn, are just some of the perks you will get by subscribing. What makes this course different from the rest of the Statistics courses out there? High-quality production – HD video and animations (This isn’t a collection of boring lectures!) Knowledgeable instructor (An adept mathematician and statistician who has competed at an international level) Complete training – we will cover all major statistical topics and skills you need to become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist Extensive Case Studies that will help you reinforce everything you’ve learned Excellent support - if you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day Dynamic - we don’t want to waste your time! The instructor sets a very good pace throughout the whole course Why do you need these skills? Salary/Income – careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow Promotions – If you understand Statistics well, you will be able to back up your business ideas with quantitative evidence, which is an easy path to career growth Secure Future – as we said, the demand for people who understand numbers and data, and can interpret it, is growing exponentially; you’ve probably heard of the number of jobs that will be automated soon, right? Well, data science careers are the ones doing the automating, not getting automated Growth - this isn’t a boring job. Every day, you will face different challenges that will test your existing skills and require you to learn something new Please bear in mind that the course comes with Udemy’s 30-day unconditional money-back guarantee. And why not give such a guarantee? We are certain this course will provide a ton of value for you. Click 'Buy now' and let's start learning together today! | 365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA 2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy 3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing 4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook 5) Blockchain for Business All of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur 365 Careers’ courses are the perfect place to start. | Business Analyst | Yes | >=4 | >=35K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
Data Science A-Z™: Real-Life Data Science Exercises Included | Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more! | 4.5 | 32551 | 208570 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Dec-22 | English | $12.99 | 21h 12m total length | https://www.udemy.com/course/datascience/ | Kirill Eremenko | Data Scientist | 4.5 | 600 | 2 | Extremely Hands-On... Incredibly Practical... Unbelievably Real! This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end. In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it! This course will give you a full overview of the Data Science journey. Upon completing this course you will know: How to clean and prepare your data for analysis How to perform basic visualisation of your data How to model your data How to curve-fit your data And finally, how to present your findings and wow the audience This course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools: SQL SSIS Tableau Gretl This course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need. Or you can do the whole course and set yourself up for an incredible career in Data Science. The choice is yours. Join the class and start learning today! See you inside, Sincerely, Kirill Eremenko | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Misc | Data Scientist | >=4 | >=30K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Machine Learning, Data Science and Deep Learning with Python | Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks | 4.5 | 28212 | 170399 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Sep-22 | English | $12.99 | 15h 36m total length | https://www.udemy.com/course/data-science-and-machine-learning-with-python-hands-on/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 138 | 668 | New! Updated with extra content on generative models: variational auto-encoders (VAE's) and generative adversarial models (GAN's) Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned! The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the A-Z of machine learning, AI, and data mining techniques real employers are looking for, including: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras Creating synthetic images with Variational Auto-Encoders (VAE's) and Generative Adversarial Networks (GAN's) Data Visualization in Python with MatPlotLib and Seaborn Transfer Learning Sentiment analysis Image recognition and classification Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multiple Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests Feature Engineering Hyperparameter Tuning ...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs. If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now! "I started doing your course... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Machine Learning | Founder/Entrepreneur | >=4 | >=25K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Python A-Z™: Python For Data Science With Real Exercises! | Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization | 4.6 | 25268 | 149508 | Created by Kirill Eremenko, Ligency I Team, Ligency Team | Dec-22 | English | $12.99 | 11h 5m total length | https://www.udemy.com/course/python-coding/ | Kirill Eremenko | Data Scientist | 4.5 | 600 | 2 | Learn Python Programming by doing! There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different! This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course! I can't wait to see you in class, What you will learn: Learn the core principles of programming Learn how to create variables How to visualize data in Seaborn How to create histograms, KDE plots, violin plots and style your charts to perfection Learn about integer, float, logical, string and other types in Python Learn how to create a while() loop and a for() loop in Python And much more.... Sincerely, Kirill Eremenko | My name is Kirill Eremenko and I am super-psyched that you are reading this! Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists. From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics. To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you! | Python | Data Scientist | >=4 | >=25K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Artificial Intelligence A-Z™: Learn How To Build An AI | Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications! | Bestseller | 4.4 | 21951 | 189908 | Created by Hadelin de Ponteves, Kirill Eremenko, Ligency I Team, Luka Anicin, Ligency Team, Jordan Sauchuk | Dec-22 | English | $16.99 | 16h 57m total length | https://www.udemy.com/course/artificial-intelligence-az/ | Hadelin de Ponteves | Serial Tech Entrepreneur | 4.5 | 297 | 1 | *** AS SEEN ON KICKSTARTER ***Learn key AI concepts and intuition training to get you quickly up to speed with all things AI. Covering: How to start building AI with no previous coding experience using PythonHow to merge AI with OpenAI Gym to learn as effectively as possibleHow to optimize your AI to reach its maximum potential in the real world Here is what you will get with this course: 1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means. 2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited. 3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line. 4. Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect. 5. In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum. | Hadelin is an online entrepreneur who has created 30+ top-rated educational e-courses to the world on new technology topics such as Artificial Intelligence, Machine Learning, Deep Learning, Blockchain and Cryptocurrencies. He is passionate about bringing this knowledge to the world and help as much people as possible. So far more than 1.6 million students have subscribed to his courses. | Artificial Intelligence | Founder/Entrepreneur | >=4 | >=20K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
Spark and Python for Big Data with PySpark | Learn how to use Spark with Python, including Spark Streaming, Machine Learning, Spark 2.0 DataFrames and more! | Bestseller | 4.6 | 20302 | 109767 | Created by Jose Portilla | May-20 | English | $15.99 | 10h 35m total length | https://www.udemy.com/course/spark-and-python-for-big-data-with-pyspark/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 981 | 3 | Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems! Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market! This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem! We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion! If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you! | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Big Data/Data Engineer | Head/Director | >=4 | >=20K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
Data Analysis with Pandas and Python | Analyze data quickly and easily with Python's powerful pandas library! All datasets included --- beginners welcome! | Bestseller | 4.6 | 18377 | 174892 | Created by Boris Paskhaver | Jul-22 | English | $12.99 | 22h 0m total length | https://www.udemy.com/course/data-analysis-with-pandas/ | Boris Paskhaver | Software Engineer | Consultant | Author | 4.7 | 35 | 345 | Student Testimonials: The instructor knows the material, and has detailed explanation on every topic he discusses. Has clarity too, and warns students of potential pitfalls. He has a very logical explanation, and it is easy to follow him. I highly recommend this class, and would look into taking a new class from him. - Diana This is excellent, and I cannot complement the instructor enough. Extremely clear, relevant, and high quality - with helpful practical tips and advice. Would recommend this to anyone wanting to learn pandas. Lessons are well constructed. I'm actually surprised at how well done this is. I don't give many 5 stars, but this has earned it so far. - Michael This course is very thorough, clear, and well thought out. This is the best Udemy course I have taken thus far. (This is my third course.) The instruction is excellent! - James Welcome to the most comprehensive Pandas course available on Udemy! An excellent choice for both beginners and experts looking to expand their knowledge on one of the most popular Python libraries in the world! Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include: installing sorting filtering grouping aggregating de-duplicating pivoting munging deleting merging visualizing and more! Why learn pandas? If you've spent time in a spreadsheet software like Microsoft Excel, Apple Numbers, or Google Sheets and are eager to take your data analysis skills to the next level, this course is for you! Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language. Pandas is a powerhouse tool that allows you to do anything and everything with colossal data sets -- analyzing, organizing, sorting, filtering, pivoting, aggregating, munging, cleaning, calculating, and more! I call it "Excel on steroids"! Over the course of more than 19 hours, I'll take you step-by-step through Pandas, from installation to visualization! We'll cover hundreds of different methods, attributes, features, and functionalities packed away inside this awesome library. We'll dive into tons of different datasets, short and long, broken and pristine, to demonstrate the incredible versatility and efficiency of this package. Data Analysis with Pandas and Python is bundled with dozens of datasets for you to use. Dive right in and follow along with my lessons to see how easy it is to get started with pandas! Whether you're a new data analyst or have spent years (*cough* too long *cough*) in Excel, Data Analysis with pandas and Python offers you an incredible introduction to one of the most powerful data toolkits available today! | Hi there, it's nice to meet you! I'm a New York City-based software engineer, author, and consultant who's been teaching on Udemy since 2016. Like many of my peers, I did not follow a conventional approach to my current role as a web developer. After graduating from New York University in 2013 with a degree in Business Economics and Marketing, I worked as a business analyst, systems administrator, and data analyst for a variety of companies including a digital marketing agency, a financial services firm, and an international tech powerhouse. At one of those roles, I was fortunate enough to be challenged to build several projects with Python and JavaScript. There was no formal computer science education for me; I discovered coding entirely by accident. A small work interest quickly blossomed into a passionate weekend hobby. Eventually, I left my former role to complete App Academy, a rigorous full-stack web development bootcamp in NYC. The rest is history. I've always been fascinated by the intersection of technology and education, especially since I've struggled with many of the traditional resources people use to learn how to program. My goal as an instructor is to create comprehensive step-by-step courses that break down the complex details into small, digestible pieces. I like to build the kind of material that I myself would have loved to have when I was starting out. I'm passionate about teaching and would love to help you discover what code can do for you. See you in a course soon! | Python | Consultant | >=4 | >=15K | >=1 Lakh | >=4 | >=10 Lakh | >=10 Lakh | |||||||||||||||||
Complete Guide to TensorFlow for Deep Learning with Python | Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! | 4.3 | 16564 | 93351 | Created by Jose Portilla | Apr-20 | English | $14.99 | 14h 9m total length | https://www.udemy.com/course/complete-guide-to-tensorflow-for-deep-learning-with-python/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 981 | 3 | Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network Basics TensorFlow Basics Artificial Neural Networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks AutoEncoders Reinforcement Learning OpenAI Gym and much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well. It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google! Become a machine learning guru today! We'll see you inside the course! | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Deep Learning | Head/Director | >=4 | >=15K | >=50K | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Apache Spark with Scala - Hands On with Big Data! | Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets, on your desktop or on Hadoop with Scala! | 4.5 | 16353 | 88208 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Sep-22 | English | $12.99 | 8h 58m total length | https://www.udemy.com/course/apache-spark-with-scala-hands-on-with-big-data/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 138 | 668 | New! Completely updated and re-recorded for Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API. “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think, and you'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Spark works best when using the Scala programming language, and this course includes a crash-course in Scala to get you up to speed quickly. For those more familiar with Python however, a Python version of this class is also available: "Taming Big Data with Apache Spark and Python - Hands On". Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course. Learn the concepts of Spark's Resilient Distributed Datasets, DataFrames, and Datasets. Get a crash course in the Scala programming language Develop and run Spark jobs quickly using Scala, IntelliJ, and SBT Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Practice using other Spark technologies, like Spark SQL, DataFrames, DataSets, Spark Streaming, Machine Learning, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to SpiderMan? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. over 8 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Enroll now, and enjoy the course! "I studied Spark for the first time using Frank's course "Apache Spark 2 with Scala - Hands On with Big Data!". It was a great starting point for me, gaining knowledge in Scala and most importantly practical examples of Spark applications. It gave me an understanding of all the relevant Spark core concepts, RDDs, Dataframes & Datasets, Spark Streaming, AWS EMR. Within a few months of completion, I used the knowledge gained from the course to propose in my current company to work primarily on Spark applications. Since then I have continued to work with Spark. I would highly recommend any of Franks courses as he simplifies concepts well and his teaching manner is easy to follow and continue with! " - Joey Faherty | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Big Data/Data Engineer | Founder/Entrepreneur | >=4 | >=15K | >=50K | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Data Science and Machine Learning Bootcamp with R | Learn how to use the R programming language for data science and machine learning and data visualization! | 4.6 | 15562 | 86338 | Created by Jose Portilla | Dec-20 | English | $12.99 | 17h 45m total length | https://www.udemy.com/course/data-science-and-machine-learning-bootcamp-with-r/ | Jose Portilla | Head of Data Science at Pierian Training | 4.6 | 981 | 3 | Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R! Here a just a few of the topics we will be learning: Programming with R Advanced R Features Using R Data Frames to solve complex tasks Use R to handle Excel Files Web scraping with R Connect R to SQL Use ggplot2 for data visualizations Use plotly for interactive visualizations Machine Learning with R, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Data Mining Twitter Neural Nets and Deep Learning Support Vectore Machines and much, much more! Enroll in the course and become a data scientist today! | Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science, Machine Learning and Python Programming. He has publications and patents in various fields such as microfluidics, materials science, and data science. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Training and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to check out the website link to find out more information about training offerings. | Machine Learning | Head/Director | >=4 | >=15K | >=50K | >=4 | >=10 Lakh | >=10 Lakh | ||||||||||||||||||
Taming Big Data with Apache Spark and Python - Hands On! | PySpark tutorial with 20+ hands-on examples of analyzing large data sets on your desktop or on Hadoop with Python! | Bestseller | 4.5 | 14042 | 84334 | Created by Sundog Education by Frank Kane, Frank Kane, Sundog Education Team | Oct-22 | English | $12.99 | 6h 57m total length | https://www.udemy.com/course/taming-big-data-with-apache-spark-hands-on/ | Sundog Education by Frank Kane | Founder, Sundog Education. Machine Learning Pro | 4.6 | 138 | 668 | New! Updated for Spark 3, more hands-on exercises, and a stronger focus on DataFrames and Structured Streaming. “Big data" analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark and specifically PySpark. Employers including Amazon, EBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You'll learn those same techniques, using your own Windows system right at home. It's easier than you might think. Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples, and then scale them up to run on cloud computing services in this course. You'll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of Spark's DataFrames and Resilient Distributed Datastores Develop and run Spark jobs quickly using Python and pyspark Translate complex analysis problems into iterative or multi-stage Spark scripts Scale up to larger data sets using Amazon's Elastic MapReduce service Understand how Hadoop YARN distributes Spark across computing clusters Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX By the end of this course, you'll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. This course uses the familiar Python programming language; if you'd rather use Scala to get the best performance out of Spark, see my "Apache Spark with Scala - Hands On with Big Data" course instead. We'll have some fun along the way. You'll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you've got the basics under your belt, we'll move to some more complex and interesting tasks. We'll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We'll analyze a social graph of superheroes, and learn who the most “popular" superhero is – and develop a system to find “degrees of separation" between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You'll find the answer. This course is very hands-on; you'll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon's Elastic MapReduce service. 7 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX. Wrangling big data with Apache Spark is an important skill in today's technical world. Enroll now! " I studied "Taming Big Data with Apache Spark and Python" with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course! " - Cleuton Sampaio De Melo Jr. | Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis. | Big Data/Data Engineer | Founder/Entrepreneur | >=4 | >=10K | >=50K | >=4 | >=10 Lakh | >=10 Lakh |