I did Andrew Ng's old Machine Learning, Obarsky's Scala course, the Ng's Deep Learning specialization, Nand to Tetris part 1 and a small Data Science course which wasn't very good. I think my very first course was "Model Thinking" course, but I never took the exam there.
I also tried the sequel to the Scala course at one point, and the Cryptography course, but I dropped out from those after finding out they were a bit too hard - I spent way more time on the coursework than I'd intended.
But I can't say I like the direction it's taken in recent years.
Didn’t the gamification course have one of the relatively few well done peer assessments? The course was good, but it’s interesting now that gamification features completely turn me off now on any platform or program attempting to motivate me toward a specific end, regardless of whether that goal is in my interest or the interest of someone else trying to make money.
And yeah: Gamification became shallow real fast. Even the Gamification techniques in games! I think the reason is that everyone focused on adopting the easy part (checkboxes, achievements, levels) while skipping the real core (player types, intrinsic motivation)... But the course even warned about this level of shallow implementation.
(Btw: A few months later I enrolled in a course on educational psychology on coursera that was supposed to showcase some SOTA techniques... They canceled it because they could not work out the details. I think academia is often just not good at pulling things off.)
The model thinking course was interesting but it should have had a follow up that was much more than a freshman survey course treatment of each model.
Reading online it seems like most people got the impression that it was establishing that all models are essentially useless. Instead it was showing that each of these models were an extremely efficient way to understand some dynamic situations, but that it’s still absurd to focus on only one model when trying to understand the world.
Agreed about Odersky, the Scala course and the Scala Functional Programming course were solid (the latter a bit less so, a blemish was its insistence on Akka, but the concepts were interesting).
There was also a very interesting introduction to Programming Languages (by Dan... something? He was from the University of Washington I think) which covered multiple paradigms and had interesting things to say about the ML family.
Yes, Programming Languages by Dan Grossman! The course was later split up into three parts, with each part focusing on a particular language/paradigm: SML, Racket, and Ruby. Definitely one of the higher quality offerings on Coursera.
The Odersky Scala course was pretty cool. It gave me a basic understanding of monoids and monads that has stuck with me. Robert Sedgewick's courses were very good. Learned a lot about graphs.
I also tried the sequel to the Scala course at one point, and the Cryptography course, but I dropped out from those after finding out they were a bit too hard - I spent way more time on the coursework than I'd intended.
But I can't say I like the direction it's taken in recent years.