Over the past few years, I've watched a few courses on Udacity, Coursera and EdX. I prefer taking ad-hoc courses to fill knowledge gaps (statistics, AI, programming, math, etc.), so I can't give a full review of the complete Nanodegrees, Certificates, XSeries, etc. I usually watch the lessons as needed without completing the entire course; mixing and matching MOOC courses with video learning sites (e.g. Datacamp, Youtube channels, Khan Academy, Egghead, etc.)
If I had to pick a MOOC platform, I prefer Udacity's more hands-on approach, but enjoy courses on EdX and Coursera. The quality of all three MOOC platforms is excellent. It's an amazing time for autodidacts!
If you're starting from scratch, without any background knowledge, the certificate programs with access to mentors are a great place to start. The curriculum is designed by industry professionals and/or experienced professors. This saves you time, keeps you focused and offers a place to get help when needed.
I found the lectures entertaining and the exercises of a much lower quality. Not enough of them, shallow and ambiguously worded.
I got something like 90% on the edx MITx probability course and was barely getting 50% for the above mentioned Stanford stat learning course for the 5 weeks of it I completed. I mention the MIT course, (which I highly recommend fwiw) only to support my view that I don't think my experience is aptitude or workload related. But as ever YMMV.
If I had to pick a MOOC platform, I prefer Udacity's more hands-on approach, but enjoy courses on EdX and Coursera. The quality of all three MOOC platforms is excellent. It's an amazing time for autodidacts!
If you're starting from scratch, without any background knowledge, the certificate programs with access to mentors are a great place to start. The curriculum is designed by industry professionals and/or experienced professors. This saves you time, keeps you focused and offers a place to get help when needed.