If you really want to take the whole path for the sake of it, a quite agreed-upon path would look something like this (it's been referred to it a lot on Reddit, I think it's called the harsh guide to ML or something):
-Take the Elements of Statistical Learning by Hastie and Tibshirani; Really great textbook that has all the in-depth mathematics for all the classical ML you need plus exercises
- you can get Andrew Ng's Coursera course, it's still really good and relevant, and it takes you through all the ML you need plus exercises in both Python and R.
- Go through the Deep Learning book, it goes through the details of DL, why and why not it is related to ML.
- After that you're more or less in open waters, you have all the background, and you're left to figure out whatever you want for yourself. To get more into newer research in computer vision, just search ArXiv for all the still relevant papers and try to o through them and understand them. Trying to implement them yourself from an official Git repo helps tremendously, so definitely do that. Also if you're more into classical ML, go on keggle and try some of the contests out and see if you can manage to do anything. At the beginning, you won't, but read through the best solutions and see what they've done.<p>Good luck with the whole process! I believe it is quite challenging to learn it all from the bare metal maths but definitely worth it and quite rewarding.