Just saying, but if you want to hop onto the ML bandwagon (for instance), then don't bother going over linear algebra or probabilities first, and instead just learn what you need as you go. For example, the first sections of this book are already devoted to getting you on the right track, and it's somewhat standard to do so. And besides, there's no need in learning what are rotation matrices if you won't use them.
As a counterpoint, if parent is interested in taking ML further, a solid foundation in linear algebra will be huge when more advanced signal processing applications come up.
Same course if you prefer the classroom lectures http://ocw.mit.edu/courses/electrical-engineering-and-comput...
Or if you want more rigor you can go through these notes that cover the same material but in a more formal way (via sigma algebras and measure theory) http://ocw.mit.edu/courses/electrical-engineering-and-comput...