[joshvm]

Yes for linear algebra, though it's probably best suited to people with at least some exposure to matrix/vector math (for example you've used SVD but have no idea how or why it works): https://www.fast.ai/2017/07/17/num-lin-alg/

For probability, machine learning is more about statistics (the two are related, but courses explicitly about probability will cover different things), so I would lean towards that. An Introduction to Statistical Learning in R (ISLR) is a frequently recommended book. You can ignore the R and do the exercises in Python.

If you actually want to learn about probability, you can look at MIT's course: https://ocw.mit.edu/resources/res-6-012-introduction-to-prob...

EDIT: If you've never been exposed to calculus, many people swear by Khan Academy's videos.

[input_device]

This is great. Thanks you!

[Canadauni]

I would also recommend the MIT calculus courses on edX. It has been a great refresher for me since the last I looked at calc was 8 years ago.

[input_device]

I'll check it out, thanks!