I'm taking both of those also, and the math in ML isn't intensive... but there is much more exposure to calculus and linear algebra in ML than in Algorithms I,

I'm taking both of those also, and the math in ML isn't intensive... but there is much more exposure to calculus and linear algebra in ML than in Algorithms I, and while I agree with the other commenter who said Andrew Ng walks you through it, an understanding of both helps with getting the "why" of things like the gradient descent formulas introduced in the linear regression lectures. The ML course does start out with an optional linear algebra review lecture, but it's very focused on mechanics rather than underlying mathematical reasoning... for conceptual mastery (or even just brushing up) I'd recommend going through Khan's videos on the subject, if you have the time.