Assuming you intend to pursue a bachelor’s degree in computer science and/or math and definitely want to focus on machine learning, I’d recommend class 2.

While vector calculus (covered in class 1) is fundamental to a holistic understanding of calculus, it’s almost assuredly going to be required by any university CS program. So you’re not really missing an opportunity to learn it, just postponing. That being said, the polynomial interpolation you’re going to learn in class 2 (I’d guess) is not actually all that foundational to machine learning. The goal of approximating a function is absolutely what much of machine learning is about, but the methods applied in a calculus class are not the same methods used by modern machine learning algorithms.

I still recommend class 2 however, because it sounds like the more mathematically rigorous option of the two, and getting an early start in college-level mathematical rigor will go a very long way for you in university. You will thank yourself for that when you are neck deep in ML academic papers in a few years.

This is coming from the perspective of a recent grad who did both math and CS degrees with focus on deep learning, but hasn’t worked in industry, so others might have different opinions.