If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice).
But it's ok to start using libraries and fitting models without understanding how they work deeply, and coming back to these books later (just make sure you come back; there's lots of useful ideas in them!) In which case I'd recommend some of the resources the parent doesn't recommend
> If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice).
This doesn't work. ISL is good, but it aims to be accessible by excluding most of the math. So if you go over it, you'll neither "deeply understand ML techniques", nor will you encounter enough math that you can learn along the way as you suggest.
But it's ok to start using libraries and fitting models without understanding how they work deeply, and coming back to these books later (just make sure you come back; there's lots of useful ideas in them!) In which case I'd recommend some of the resources the parent doesn't recommend