Best Online Courses on Machine Learning and AI in 2022

Machine Learning (ML) and Artificial Intelligence (AI) are growing exponentially. AI is the skill of the future. It has been estimated that by 2030, the AI market will contribute more than $15 trillion to the global economy. On the other hand, ML is becoming one of the most exciting and fast-paced computer science fields. In this post, we will go through the best online courses on Machine Learning and AI for beginners.

Why should you Learn Machine Learning and AI Skills?

Every business is trying to implement AI in its processes and products. Learning AI can therefore open a world of opportunities for anyone. A combination of ML, AI, and deep learning can chart out a way to great career prospects.

Even Google’s CEO Sundar Pichai recently made the bold statement, “AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire.” Wow!

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.

Best Online Courses on Machine Learning, Deep Learning, AI

8 Best Online Courses on Machine Learning and AI for Beginners

Co-authored by Tanmoy Ray

Machine Learning (Stanford University)

Students enrolled: 4.6 Million+

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. It’s a great machine learning ai certification by Stanford University (from Coursera).

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

Topics include:

  1. Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
  2. Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
  3. 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.

Machine Learning A-Z: Hands-On Python & R in Data Science

Students Enrolled: 265,000+ Students

Average Rating: 4.4

It’s a great course for 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.

Machine Learning Specialization (University of Washington)

Students enrolled: 120,000+

Average Rating: 4.7

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 a period of multiple weeks. Key areas covered in the course include Clustering, Information Retrieval, Prediction, Classification of all other relevant topics.

Python for Data Science and Machine Learning Bootcamp

Students Enrolled: 128,000+

Average Rating: 4.5

It’s a comprehensive course to learn python online, 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.

Data Science, Deep Learning and Machine Learning with Python

Students enrolled: 159,000+

Average Rating: 4.6

Another top-notch course 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 powers movie and product recommendations that influence millions of people around the world.

It provides a complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks

This course will also 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.

Deep Learning Specialization

Students enrolled: 657,000+

Average Rating: 4.9

If you have read our previous article on Data Engineering and Deep Learning Jobs, you will know that Deep Learning is the way to go if you want to make a career in AI.

Best Online Courses on Machine Learning AI

This course is developed by Andrew Ng in association with Stanford Professors and NVIDIA & 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.

What will you learn?

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 the 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.

Deep Learning A-Z™: Hands-On Artificial Neural Networks

Students enrolled: 326,000+

Average Rating: 4.6

It’s another great course to learn Deep Learning Algorithms in Python from two Machine Learning & Data Science experts.

What will you learn?

  • Understand the intuition behind Artificial Neural Networks
  • Apply Artificial Neural Networks in practice
  • Understand the intuition behind Convolutional Neural Networks
  • Apply Convolutional Neural Networks in practice
  • Understand the intuition behind Recurrent Neural Networks
  • Apply Recurrent Neural Networks in practice
  • Understand the intuition behind Self-Organizing Maps
  • Apply Self-Organizing Maps in practice
  • Understand the intuition behind Boltzmann Machines
  • Apply Boltzmann Machines in practice
  • Understand the intuition behind AutoEncoders
  • Apply AutoEncoders in practice

Machine Learning (Columbia University)

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.

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