[eachro]

I think it depends on what you want to focus on. If you want to do deep learning, fast.ai is probably the best resource available. Jeremy Howard and Rachel Thomas (the two founders) have poured quite a lot into fostering a positive, supportive community around fast.ai which really does add quite a lot of value.

If you want to really understand the fundamentals of machine learning (deep learning is just one subset of ML!), there is no substitute for picking up one of the classic texts like: Elements of Statistical Learning (https://web.stanford.edu/~hastie/ElemStatLearn/), Machine Learning: A Probabalistic Approach (https://www.cs.ubc.ca/~murphyk/MLbook/) and going through it slowly.

I'd recommend a two pronged approach: dig into fast.ai while reading a chapter a week (or at w/e pace matches your schedule) of w/e ML textbook you end up choosing. Despite all of the hype of deep learning, you really can do some pretty sweet things (ex: classify images/text) with neural nets within a day or two of getting started. Machine learning is a broad field, and you'll find that you will never know as much as you think you should, and that's okay. The most important thing is to stick to a schedule and be consistent with your learning. Good luck on this journey :)

[jamesxv7]

Excellent recommendation. I really appreciate all the recommendations proposed. Happy New Year eachro.