Machine Learning (ML), Artificial intelligence (AI) and Analytics are growing exponentially and reshaping our lives. The emergence of artificial intelligence (AI) has played a key part in ushering in a Fourth Industrial Revolution. With increasing automation, the demand for machine learning and artificial intelligence skills has also gone up by 60% in India. This has also made the young folks going after online courses in machine learning, data science & analytics in numbers.

The iterative aspect of ML is important because as models are exposed to new data, they adapt independently. They learn from previous computations to produce reliable, repeatable decisions and results. ML and Deep Learning are specialized fields of AI. It’s a science that’s not new – but one that has gained fresh momentum due to the rapid emergence of big data and analytics. If you are new to these technologies, then refer to the Introduction (Beginners Guide) to Data Science, ML, AI, Big Data Analytics, Deep Learning, ANN, NLP, IoT, Cybersecurity & Blockchain Technology first.

Google’s self-driving car, cyber fraud detection, disease diagnosis by IBM’s Watson, online recommendation engines – like friend suggestions on Facebook and YouTube or Netflix showcasing the movies and shows you might like, and “more items to consider” and “get yourself a little something” on Amazon—are all examples of applied machine learning. ML, AI & big data analytics are expanding and complex fields.

Related Post: 18 Best Online Courses for Data Science in 2019

In this post, we will look at the best online courses on machine learning, deep learning, AI, and big data analytics. All these courses are suitable for beginners, intermediate learners, and the pros as well.

18 Best Online Courses on Machine Learning, Deep Learning, AI and Big Data Analytics

Average Rating: 4.9

Stanford University’s Machine Learning on Coursera is the clear current winner in terms of ratings, reviews, and syllabus fit. This course is also taught by Andrew Ng.

Released in 2011, it covers all aspects of the machine learning workflow. Though it has a smaller scope than the original Stanford class upon which it is based, it still manages to cover a large number of techniques and algorithms. The estimated timeline is eleven weeks, with two weeks dedicated to neural networks and deep learning.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

Topics include:

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)

The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

You will learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

Sign up for the course here.

Average Rating: 4.8

Columbia University’s Machine Learning is a relatively new offering that is part of their Artificial Intelligence MicroMasters on edX.

This course covers all aspects of the machine learning workflow. In fact, it covers more algorithms than the above Stanford course. The course has got a more advanced introduction, with reviewers noting that students should be comfortable with the recommended prerequisites (calculus, linear algebra, statistics, probability, and coding).

The course is taught by Prof. John W. Paisley from the Dept. of Electrical Engineering.

Topics include classification and regression, clustering methods, sequential models, matrix factorization, topic modeling, and model selection.

Methods include linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

Enroll for the course here.

Students Enrolled: 265,000+ Students

Average Rating: 4.4

It’s great courses for the learners who aim to learn python for machine learning and data science. The course has been created by Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, and SuperDataScience Support. This course will help you Master Machine Learning on Python and R, make accurate predictions, build a great intuition of many machine learning models, handle specific tools like reinforcement learning, NLP, and Deep Learning. Most importantly it teaches you to choose the right model for each type of problem.

Basic high school mathematics is all you are supposed to know to take up this course. It’s an impressively detailed offering that provides instruction in both Python and R, which is rare and can’t be said for any of the other top courses. With 40 hours of learning (including on-demand video) + 19 articles, it’s one of the best online courses on machine learning and data science.

As a “bonus,” the course includes Python and R code templates for students to download and use on their own projects. There are quizzes and homework challenges, though these aren’t the strong points of the course.

Sign up for the course here (discount going on).

This course is suitable easier for the folks without strong technical backgrounds, in comparison to the courses by Stanford and Columbia.

Taught by Emily Fox and Carlos Guestrin, both Amazon Professors of Machine Learning, it is a comprehensive course spread over the period of multiple weeks. Key areas covered in the course include Clustering, Information Retrieval, Prediction, Classification of all other relevant topics.

Click here to enroll for the course.

Students Enrolled: 128,000+

Average Rating: 4.5

It’s a comprehensive python online course, developed by Jose Portilla (Santa Clara University) and Pierian Data International. It has large chunks of machine learning content but covers the whole data science process. It’s more of a very detailed introduction to Python.

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more. It’s of the best in the market if you are looking at an introduction to machine learning with python.

Sign up for the course here (discount going on).

Average Rating: 4.8

It’s an extremely intense online program for machine learning from Springboard. Students will need to devote 15-20 hours per week to complete the course in 6 months (~400 hours in total). It’s an ideal course for the folks at least 1 year of professional experience in software engineering.

Students must have knowledge of the following topics at the college level:

Linear Algebra

Probability and Descriptive statistics

Calculus

One of the unique features is that the students will also receive 1:1 mentoring and career support with jobs and career progression. Tutors and mentors include machine learning engineers and data scientists from Facebook, Instacart, Jawbone, AdRoll and so on.

The course includes a total of 9 units. They are:

Unit 1: Overview of AI and Machine Learning Engineering Stack

Unit 2: Data Wrangling at Scale and Statistics for AI

Unit 3: Foundation of Machine Learning

Unit 4: A Deep Dive into Deep Learning

Unit 5: AI Case Study 1: Natural Language Processing

Unit 6: AI Case Study 2: Computer Vision

Unit 7: Building and Deploying Large-Scale AI Systems

Unit 8: Capstone Project

Unit 9: Career Support

Sign up for the course here.

Students Enrolled: 35,000+

Average Rating: 4.5

Another highly rated and recommended online courses by Jose Portilla. This course will cover a variety of topics including Neural Network, Deep Learning & Machine Learning, TensorFlow, Artificial Neural Networks, Convolutional & Recurrent Neural Networks, AutoEncoders Reinforcement Learning and more.

You will be taught how to build your neural network from scratch with Python, using TensorFlow for a variety of applications such as Image Classification with Convolutional Neural Networks, Time Series Analysis with Recurrent Neural Networks and solving Unsupervised Learning Problems with AutoEncoders.

Some knowledge of programming (preferably Python) is required for this course.

Sign up for the course here.

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Average Rating: 4.5

Students Enrolled: 61,500+

Another top-notch courses to learn python online. The course has been created by Frank Kane, who spent 9 years at Amazon and IMDb, developing and managing the technology that automatically that powers movie and product recommendations which influence millions of people around the world.

This course will help you extract meaning from large datasets using a wide variety of data science, data mining and machine learning techniques using Python. Along with that, you will get to apply your learning as well.

Sign up for the course (discount available).

Average Rating: 4.6

Students Enrolled: 28,300+

This is one of the most comprehensive courses for machine learning and data science that concentrates on R. You will learn how to program with R, to create amazing data visualizations, and use Machine Learning with R. You will also learn Programming with R, Advanced R Features, Using R Data Frames to solve complex tasks, using R to handle Excel Files, Web scraping with R, Connecting R to SQL and more.

Click here to sign up for the course (discounted price).

Students Enrolled: 100,600+

Average Rating: 4.4

Artificial intelligence is growing exponentially. 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.

Deep Learning and Artificial Neural Networks help to solve complex problems and hence they form the core of Artificial intelligence. Created by Kirill Eremenko and Hadelin de Ponteves, this is one of the best courses on Deep Learning and Neural Networks.

In these courses, you will get to know to understand Artificial Neural Networks, Recurrent Neural Networks, Self Organizing Maps, Boltzmann Machines, and Auto-Encoders; and also how to apply them.

Sign up for the course.

Average Rating: 4.9

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought-after skills in tech. We will help you become good at Deep Learning.

This course is developed by Andrew Ng in association with Stanford Professors and NVIDIA & deeplearning.ai as industry partners. Andrew Ng is the Co-Founder of Coursera and has headed the Google Brain Project and Baidu AI group in the past.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. You will also get to work on real-time case studies around healthcare, music generation and natural language processing among other industry areas.

It’s one of the best courses to learn the foundations of Deep Learning, how to build neural networks and how to build machine learning projects.

Click here to enroll for the course.

It’s more focused on analytics in general, though it does cover several machine learning topics. It’s an intensive course (10 – 15 hours per week) that leverages familiar real-world examples. The course has been created (and taught) by Dimitris Bertsimas, Boeing Professor of Operations Research.

You will learn:

An applied understanding of many different analytic methods, including linear regression, logistic regression, CART, clustering, and data visualization

How to implement all of these methods in R

An applied understanding of mathematical optimization and how to solve optimization models in spreadsheet software

Sign up here to enroll for the course.

Average Rating: 4.5

The course introduces the core concepts of machine learning and a variety of algorithms. It uses both Python and R and leverages several big data-friendly tools, including Apache Spark, Scala, and Hadoop. Four hours per week over six weeks.

You will learn:

Using Spark to explore data and prepare for the modeling

Build supervised machine learning models

Evaluate and optimize models

Build recommenders and unsupervised machine learning models

To complete the hands-on elements in this course, you will require an Azure subscription and a Windows client computer. You can sign up for a free Azure trial subscription (a valid credit card is required for verification, but you will not be charged for Azure services).

Click here to sign up for the course.

Average Rating: 4.5

Students Enrolled: 14,850

If Python or R isn’t your cup of tea, this training helps you learn Scala and Spark for Big Data and Machine Learning. The course focuses on “Big data”, specifically on implementation in Scala and Spark. It will act as a crash course in Scala Programming, Spark and offer a Big Data Ecosystem overview using Spark’s MLlib for Machine Learning.

Enroll now.

Average Rating: 4.4

Students Enrolled: 4,200+

It’s a unique focus on cloud-based machine learning and specifically Amazon Web Services.

You will learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization. You will also learn to integrate predictive models with your application using simple and secure APIs.

If you are preparing for certification, this course will be very useful as you will learn best practices and gain hands-on experience in securely deploying products using AWS Cloud.

Sign up for the course.

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit.

A Capstone Project brings together the course material with a realistic recommender design and analysis project. The capstone project includes a case study. You will be taught using LensKit (an open-source toolkit for recommender systems).

Sign up here.

Average Rating: 4.6

Students Enrolled: 15,290+

This is for the folks with intermediate level knowledge on the subject. To get started, you will need to have some understanding of Calculus, Probability, Markov Models, Numpy Stack and some experience with few supervised ML methods. This one is clearly not for the beginners, but for all those looking at some serious insights and preparing for the future, this is it.

The trainer ‘Lazy Programmer’ has many great courses to his name and some of the titles include Linear Regression in Python, Logistic Regression in Python, Practical Deep Learning in Theano and TensorFlow, Unsupervised Deep Learning, Recurrent Neural Networks in Python Artificial Intelligence.

Average Rating: 4.7

Students Enrolled: 13,700+

If you want to master Artificial Intelligence using Deep Learning and Neural Networks, then this is the right choice for you. Learn to use advanced reinforcement learning algorithms for a variety of problems and understand Reinforcement Learning with RBF Networks. You should be aware of reinforcement learning basics, Dynamic Programming and/or TD Learning in order to enroll in this program.

The above two courses are created and taught by Lazy Programmer. Check out the Machine Learning Series (Total 21 Courses) by Lazy Programmer Inc.

Related Posts on Classroom-Based/Full-Time Degree Programs:

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Best Masters (MS) Programs for Data Science and Big Data Analytics in Canada

Top Masters Programs for Data Science, Machine Learning, and Analytics in Europe – Part 1

Top Masters Programs for Data Science, Machine Learning, and Analytics in Europe – Part 2

Sources: 1, 2, 3.