A lot of the advice you will get is going to be in the spirit of the American way of teaching various scientific disciplines. I would decribe it as a hands on a

A lot of the advice you will get is going to be in the spirit of the American way of teaching various scientific disciplines. I would decribe it as a hands on approach, that gets you started really fast and doing exciting things. This is true of computer science, statistics, and physics as far as I know. It's probably a good way to go about learning these things. The French method for teaching these things (the one I'm used to) is to first do a lot of mathematics and then to introduce these topics through powerful mathematical formalisms, from the get go. All else equal, for most people, it's probably a worse approach. However, the American way does err a little bit on the side of too little mathematics, and it can come and bite you. My suggestion would thus be to follow all the advice given, but give 50% more emphasis on mathematics than people would otherwise suggest. Fortunately, the mathematics of machine learning aren't too complicated, but you want to be able to breathe that stuff. In particular this means: - linear algebra - real and complex analysis - multivariate calculus - topology (just a bit) - probability theory It's like touch typing. It may be tedious at first, but once you know this stuff really well, learning new things will be a breeze.