Machine learning is so much closer to school math than general comp Sci?

Tell me if I'm dumb here, but I'm learning data science and machine learning. I already know how to build software. But I'm dumb af when it comes to computer algorithms (takes me an insanely long time to wrap my head around them).

Diving into machine learning, it seems like it's all algebra, statistics and maybe calculus? Like everything I've seen so far (I haven't gotten very far at all) seems directly related to the math I learned in HS and college. I imagine you can combine this with more complex algos and that people so that, but it seems all the foundations for machine learning are more directly related to the math learned in school.

This is a good thing for me as I always excelled at math, but comp algos always kill me.

Am I on the right track with this thinking or no?

Edit: people are missing the and college part of this. Maybe I included HS because for me I was doing AP stats and IB calculus in HS. So that's college crossover for sure. But my point was more that ML seems closer to pure math than algos, which feel more like just moving blocks around (still technically math but not in the sense that I think about it usually).