I'm taking this course right now, I'm a little ambivalent about it. They cover various machine learning algorithms, which one can learn anywhere, but they also talk about how to deal with these things in a massive data context. The pragmatic tools needed to wrangle large amounts of data so that you can apply your usual ML algorithms to it is very nice to see.

That said, I don't feel like I'm learning concepts. So far, the techniques have felt like: break up the data into chunks this way, apply a bunch of hash functions that way, this is what ended up working for this particular problem. I guess if you work in the field, the tools you're exposed to will inspire things in your own work, and you'll feel more like you're building a general framework.

The homeworks are terrible. There are no mandatory programming assignments, and the one optional one does nothing to gradually work up to applying the stuff they teach you, it's just, here's a massive zip that won't fit on your hard drive, here's an uninteresting computational question to answer about it, go for it.

The remaining (basic) homeworks are quizzes and they're incredibly tedious. (There are advanced homeworks as well, but it hasn't been that inspiring). One of the recent homeworks was really just a rehash of some high school linear algebra, another one involved doing some computations with a bunch of different points. The points weren't provided in a list, they were drawn onto a jpeg so you had to manually copy all of them down. That's the kind of course it's been.

It's a very light weight course, which is nice if you're working. If your basic math skills are good and you already have some familiarity with ML and distributed computing, 5 hrs/wk is enough to watch the videos (at 2x speed plus occasionally hitting the 10-second-fast-forward) and do the basic homeworks.