Thank you!
I might be completely wrong, but as far as my experience goes, the majority of relevant ML models rely on the basics. Of course, you can always go into hardcore proofs and complex ML models that require a lot more complex math (e.g. geometric DL or equivariant networks) but I do believe that for the majority of people getting into ML, the fundamentals are enough (again, depending on how deep you want to go into the fundamental research aspect).

I hope this somewhat makes sense :)