Calculus, by James Stewart. I recommend you buy a dead tree copy of an old edition (I paid $5 for mine, shipped. I don't know how they made money off that sale.) Might not be the best book, but it covers everything you need to know, has good exercises, everyone uses it, you can find the solutions manual online, and the harder problems have an answer on stack overflow.

And some linear algebra book by Strang (as others have said.) I don't remember which one I learned from, but I do remember it was excellent.

I can't recommend a good stats/probability book. People recommend Statistical Inference by Casella and Berger, although I wasn't super impressed with it.

You'll also want a good understanding of set theory and proof writing. I don't remember what book I learned from, but try to find some introduction to math thought.

This is all assuming you have a good understanding of algebra. The above mentioned books require geometry/trig, but you don't actually need that for machine learning.