I just finished Andrew Ng’s course as well, and had a similar experience to you. I do have a math background, so in retrospect it was probably a mistake to take it, but I saw it recommended so highly by people. I think the main value I got from it was the heuristics for debugging models and such, but I’m left wondering how many of those are even still relevant.
I’m still trying to learn ML though, so I’ll take a look at your CS+ML guide. I remember trying fastai a few months ago and I felt there like I wasn’t learning much there either, again other than debugging heuristics. I also don’t like their special library, because I can’t remember which things are part of the library and which are just pytorch (they’re essentially teaching you two libraries at once, plus all the ML concepts—it’s kind of a lot to keep in your head). Maybe I’ll take another crack at it.
If you want another guide to pull from, I was following this one a few months ago. It stood out to me from the millions of other “86 bajillion books to learn computer science NOW” lists online because they intentionally limited it to a few subjects, and give their reasoning for each choice (and the reason some other popular books may be bad choices). It’s much more CS focused, rather than programming focused, which is why I’m not following it now, but I plan to return to it when I actually have a job :)
I just finished Andrew Ng’s course as well, and had a similar experience to you. I do have a math background, so in retrospect it was probably a mistake to take it, but I saw it recommended so highly by people. I think the main value I got from it was the heuristics for debugging models and such, but I’m left wondering how many of those are even still relevant.
I’m still trying to learn ML though, so I’ll take a look at your CS+ML guide. I remember trying fastai a few months ago and I felt there like I wasn’t learning much there either, again other than debugging heuristics. I also don’t like their special library, because I can’t remember which things are part of the library and which are just pytorch (they’re essentially teaching you two libraries at once, plus all the ML concepts—it’s kind of a lot to keep in your head). Maybe I’ll take another crack at it.
If you want another guide to pull from, I was following this one a few months ago. It stood out to me from the millions of other “86 bajillion books to learn computer science NOW” lists online because they intentionally limited it to a few subjects, and give their reasoning for each choice (and the reason some other popular books may be bad choices). It’s much more CS focused, rather than programming focused, which is why I’m not following it now, but I plan to return to it when I actually have a job :)