It’s become a trend for machine learning resources to differentiate themselves by claiming to focus more on the practice, and less on the theory. My reaction to this is similar to when software teams list their focus on agile development, instead of the waterfall approach, as a key differentiating factor. Everyone’s doing it now. It isn’t differentiating anymore.
I won’t dwell on the dismal state of linear algebra in the applied fields, since I already did that here, but it needs specific mentioning that very few machine learning authors are able give a set-theoretic account of the objects involved in machine learning.
So I’m going to try. Not necessarily because I think that this description is better, per se, but because this description helps to clarify some core concepts, and I think leads to some key insights as well.