If you revisit all of a standard Calculus or Linear Algebra curriculum you will WASTE time. Learn the relevant math taught in the ai courses or the beginning chapters of deep learning books, not the irrelevant 90% of each introductory course. I say this as someone who actually used to build neural networks from scratch around 10 years ago and lost interest.
Also, if you know Calculus you can dive into approximation theory (e.g: Padé Approximations), which is a beautiful subject that lies in the intersection of Calculus and Linear Algebra.
In any case "Schaum's Outline of Linear Algebra" is probably _the_ best book on Linear Algebra I've ever read. It even touches on bits of Abstract Algebra.
It highly depends on what do you actually want.
1. Use existing models. The easiest is web services (mostly payed). Harder way is local install, still need a good computer
2. Understand how models work
3. General understanding where all this is going.
4. Being able to train or finetune existing models
4.1 Create some sort of framework for models generation
4.2 frameworks for testing, training, inference, etc..
5. Models design. They are very different depending on the domain. You will have to specialize if you want to get deeper.
6. Get AGI finally.
All things are different. Some require just following the news, some need coding skills, others more theory, philosophy. You can't have it all. If you have no relevant skill the first 4 are still withing the reach. Oh, yes. You can become ethic 'expert', that's the easiest.
Parent> If you want to learn to BUILD AI, ChatGPT's recommendations are a good start
you> what did ChatGPT recommend?
I think your token window is a bit too small.
If you just want to tinker around with models and try it out, feel free to go into it without much math knowledge and just learn them as you go. ChatGPT's recommendation is great if you have a multiyear horizon/plan to be in ML (e.g. perfect for a college student who can take courses in stats/ML side by side) or have plenty of time.