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.
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.
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.
i did linear algebra and calculus using strang and spivak textbooks. Those were classes i enjoy the most. But most of that stuff has atrophied from my brain over the years, do you recommend redoing those courses fast or can i learn when i need it on demand basis.
You can try a refresher on Jacobians. If you're following everything there well enough, you probably have what you need to move forward (and pick up the rusty parts that you need as you go). If you're completely lost then you probably want to go back for a quick refresher.
The main concepts are matrix multiplication and derivatives and their significance. Then you can dig into the specifics and review or expand your knowledge as needed.
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.
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.