[raz32dust]

I personally consider Linear algebra to be foundational in AI/ML. Intro to Linear algebra, Gilbert Strang. And his free course on MIT OCW is fantastic too.

While having strong mathematical foundation is useful, I think developing intuition is even more important. For this, I recommend Andrew Ng's coursera courses first before you dive too deep.

[mindcrime]

Another interesting resource for Linear Algebra is the "Coding the Matrix" course.

http://codingthematrix.com/

https://www.youtube.com/playlist?list=PLEhMEyM9jSinRHXJgRCOL...

[viscanti]

Strang is great but he covers a lot of things that don't have much carryover to AI/ML and doesn't really cover things like Jacobians which do. Maybe there's something more useful for someone who is only learning Calculus and Linear Algebra for AI/ML than what Strang teaches.

[AadiBugg]

These two O'Reilly books might be exactly what someone like this needs: https://www.oreilly.com/library/view/practical-linear-algebr... https://www.oreilly.com/library/view/essential-math-for/9781...

[nephanth]

Linear algebra, and differential calculus (needs linear algebra), and a bit of optimisation (at least get an understanding of sgd)

Also proba/statistics! Without those you can end up doing stuff pretty wrong

[mfrieswyk]

I never took beyond Precalculus in school, thanks for the tip!

[NationalPark]

Many of the suggestions so far are assuming you have taken undergraduate linear algebra and calculus. I'd start with those two subjects, you really can't build a foundational understanding of modern AI techniques without them.

[p1esk]

Oh, most recommendations here assume stem college math knowledge. You should become comfortable with calculus, linear algebra, and probability/stats - those are the foundations of ML.