At that level, they assume you are pretty smart and capable of figuring out something like an API on your own time as needed. They'd rather you know what all these funky things in these APIs are doing at a core level so that you can employ them in an effective manner.
With a framework it's hard to think outside the frame. With a low level core it's hard to do anything really. It's a matter of compromise. Noone starts writing asm to begin with, although it is interesting, e.g. nand tetris being a famous example.
The other is to learn the foundations of those algorithms so you can best understand how to tune, apply and extend them. This is the path to mastery.
Ironically, mere exposure to data is enouhg to learn from in ML, so why not here. Although, I'm not sure about the pedagogic aspect. I'd assume calculation by heart would be learned along the way, despite sending the initial message, it wasn't needed. Maybe starting slow is important, because it's that fundamental. But in hindsight, I really was good after half the elementary training.
I have a similar anecdote: I wasn't good at handwriting and always claimed I wouldn't need to. Now I don't need to, indeed, except for exams. But I actually have a hard time with caligraphy and that's a shame.
Your anecdote is irrelevant because there is no _understanding_ to be gained by handwriting as opposed to typing.
I really can't be bothered with this conversation, sorry.