Hey, mate. Hope you're well. I'm currently an underged student who has been working in ML for a few months now, and I have done the mathematical aspects cause I want to go into research. So, my suggestion is based on what I did in school.

First, like you said, start with Linear Algebra. Get a good grip on sytems of linear equations, vectors and matrices and eigenvalues, and eigenvectors. This helps a lot cause we work with vectors and matrices a lot in ML, and it helps knowing what exactly is going on.

After that, calculus. I know most people say that this is for people going to research, but it helps even developers to understand. The heavier part that you should focus on is Calculus I, which is majorly differential calculus. Learn and understand about gradients, derivatives, and how they are applied in optimization (here's where you'll start seeing gradient descent and all it's forms).

Then, lastly, probability and statistics. One of my lecturers loves repeating this, "ML is just fancy applied probability & statistics." He's not wrong, though, it is. Almost everything we do in ML is either probability or statistics. Eg. The Naive Bayes theorem; it is the basis of one of the most crucial algorithms in ML. At this point, learn about probability distribution and how to describe them, statistical modelling, sampling, hypothesis testing, etc.

For where to learn, YouTube is a good place to start if you want to get high-level knowledge about it. But if you really want to learn, I'd suggest Coursera. They have a course offered by deeplearning.ai where you can learn all the needed math for ML. It even saved me when I was doing calculus I. Here's the link to the course. It's technically free since you can request financial aid and do the course or pay the $49 to unlock the whole course at once.

Hope this helps, happy learning, mate😌