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Not a video course, but an excellent book: "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Aurélien Géron. Provides a gentle and high quality introduction to Machine Learning with practice from the very beginning. I wish I had this book when I was starting. It explains everything from data engineering, through how algorithms work, to practical applications. Everything in Python 3, covering pandas, scikit-learn, tensorflow. It is absolutely wonderful!

[1] http://shop.oreilly.com/product/0636920052289.do

As a person who did a lot of Deep Learning... learning in 2017, I think this was my favorite.

Fast.ai was fine, but I felt like most of my learning for the things I cared about came from reading research papers, watching Karpathy's CS231n lectures, and blog posts that went into detail on particular concepts.

But when at certain points I felt confused on certain concepts, Geron's book did a pretty good job explaining things slowly and in great detail, especially with respects to the code he wrote. It's still a book I'll pick up for 20-40 minutes every other day to help my mind recall about how something works.

Funnily enough, I've spent the last few months reading Sutton/Barto's Intro to Reinforcement Learning (along with Silver's lectures on DeepMind's YouTube Channel) and only realized Geron touches upon RL a little bit in the latter part of the ML book.

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