Resources for learning AI & ML

Online Resources

• Machine Learning by Andrew Ng (Standford University): This is where you should start from. Practice and submission of assignments is much more important than watching the videos. Don't skip the assignments.

• Deep Learning Specialization by Andrew Ng: This is probably the best resource to begin with if you want to learn deep learning. Practice and submission of assignments is much more important than watching the videos. Don't skip the assignments. If you can't pay the fees, you can request for a fee waiver from Coursera or alternately first watch the videos and then do all the assignment in the 7-day trial period for each course in this specialization.

• Shervine Amidi's VIP Cheat Sheets: The cheat sheets provided by Shervine Amidi are very helpful to quickly revise basics in AI, ML, DL, Probability & Statistics, ODE etc.

• Awesome Python Machine Learning: A curated list of awesome Python frameworks, libraries, software and resources for Machine Learning.

• Machine Learning Crash Course with TensorFlow APIs: Google's fast-paced, practical introduction to machine learning.

Articles

• The mostly complete chart of Neural Networks, explained

Books

• Deep Learning. An MIT Press book by Ian Goodfellow and Yoshua Bengio and Aaron Courville

• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (The MIT Press)

Machine Learning and Control Systems

• Data-Driven Modeling & Scientific Computation - by J. NATHAN KUTZ: This book is the one of the best books to learn the fundamentals and develop efficient coding skills. I highly recommend this book for building your basics.

• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control - S. L. Brunton and J. N. Kutz: databookuw.com/. Brunton and Kutz are doing a great work in this field. However, what is remarkable, is Brunton's teaching skills with practical coding demonstrations and lessons in his books and video lectures as well.