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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	Course Description	TrainerProfileOnUdemy	Trainer Profile Summary	Other Categories	Trainer Level	Statistics	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
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