Fwiw, my experience with MOOCs/OCW has been extremely positive (mainly in math and physics). Regarding the issue of insufficient depth, for a given ‘popular topic’ like ML, there are indeed strong incentives for lots of folks to put out courses on these, so there’ll be lots to choose from, albeit with a wide variance in quality—c.f. all the calculus courses on edX.
That said, I find that as you move away from ‘intro level’, courses are generally of a more consistent quality, where they tend to rely a lot less on a gimmicky MOOC structure and follow much more closely the traditional style of lecture --> reading --> exercises/psets. I find this to be true of things that are about equivalent to courses you’d take in ~3rd year uni—e.g. “Introduction to Operating System Scheduling” as opposed to the “Introduction to Coding” courses or whatever the equivalent is for your area of study. If you start to look at more advanced/specific courses, you may find the coverage quality improves.
I definitely can’t promise this would work for ML, but I’ve found it useful to think about what I want to learn from a course (concepts? technique? ‘Deep understanding’?) before actually searching for one. This provides a good gauge that you can usually figure out if a course meets within a few minutes, and I think may be especially relevant to ML where courses are fairly strongly divided along a tradeoff of ‘practice-heavy’ vs ‘theory-heavy’.
It’s worth noting though, that my physics/math perspective might not be as valid in learning ML, as in the former the most effective way to learn is more-or-less by following some traditional course of studies, whereas the latter has a lot of different websites which teach both in technique and theory at a basic-intermediate level; I’d be surprised if they, combined with projects, were less effective than MOOCs at the level for which they’re written. As others have noted, it may be worth looking at OpenCourseWare—MIT’s being the most extensive that I’m aware of. They also offer a ‘curriculum map’ so you can get a feel for which courses have prerequisites and what the general skillset of someone taking a given class should be.
Fwiw, my experience with MOOCs/OCW has been extremely positive (mainly in math and physics). Regarding the issue of insufficient depth, for a given ‘popular topic’ like ML, there are indeed strong incentives for lots of folks to put out courses on these, so there’ll be lots to choose from, albeit with a wide variance in quality—c.f. all the calculus courses on edX.
That said, I find that as you move away from ‘intro level’, courses are generally of a more consistent quality, where they tend to rely a lot less on a gimmicky MOOC structure and follow much more closely the traditional style of lecture --> reading --> exercises/psets. I find this to be true of things that are about equivalent to courses you’d take in ~3rd year uni—e.g. “Introduction to Operating System Scheduling” as opposed to the “Introduction to Coding” courses or whatever the equivalent is for your area of study. If you start to look at more advanced/specific courses, you may find the coverage quality improves.
I definitely can’t promise this would work for ML, but I’ve found it useful to think about what I want to learn from a course (concepts? technique? ‘Deep understanding’?) before actually searching for one. This provides a good gauge that you can usually figure out if a course meets within a few minutes, and I think may be especially relevant to ML where courses are fairly strongly divided along a tradeoff of ‘practice-heavy’ vs ‘theory-heavy’.
It’s worth noting though, that my physics/math perspective might not be as valid in learning ML, as in the former the most effective way to learn is more-or-less by following some traditional course of studies, whereas the latter has a lot of different websites which teach both in technique and theory at a basic-intermediate level; I’d be surprised if they, combined with projects, were less effective than MOOCs at the level for which they’re written. As others have noted, it may be worth looking at OpenCourseWare—MIT’s being the most extensive that I’m aware of. They also offer a ‘curriculum map’ so you can get a feel for which courses have prerequisites and what the general skillset of someone taking a given class should be.