Have to agree with all of this. I've taken Andrew Ng's Machine Learning course (only time I've paid for a 'verified' certificate), and found it a great overview of ML, though I'm not sure I'd feel comfortable telling anyone I have a good understanding of ML :)
Odersky's FP in Scala was actually the first Coursera course I took (during its initial run, I think). -- I also found the follow up Reactive Programming course to be excellent as well.
While I'm not perfect, I spent nearly 3 years teaching at a bootcamp fulltime, so perhaps I have different standards for communicating and teaching actionable lessons?
Couldn't agree more. I took this course in 2011 but didn't have a need for working with databases until 2014. Three years after I took the course I was able to jump in and work fluently on databases -- Mongo, Sqlite, Firebase, etc. The least I can say is the course helped me internalize database concepts.
This course has the right amount of handholding yet challenges you enough so you acquire long-lasting skills.
I've now built 2 data warehouses and helped with maintenance on another. It's not my main focus, but it's nice to be able to do it myself when the work calls for it.
I got hired at my first job out of college due to the C# ones and I've been working in C# since then, with a smattering of other languages here or there.
Main page: https://cs.stanford.edu/people/widom/DB-mooc.html
I studied portions of it before I got my first "real" tech job (not a call-center) and it put me on a path straight towards my career as a developer. I go back to the course every year or so and try to pick more gems out of it -- it's truly fantastic.
[1] https://www.notion.so/MOOCs-recommended-by-Hacker-News-e1070...
EDIT: the previous link pointed to one of the headers in the page, instead of the page itself.
– the first course in the specialization has a very good and engaging start, but the gap between lectures and problems widens quickly after that (maybe that's why the author boasts about a "challenging" course "not for everyone"). I'm hesitant to take the next course of the specialization.
On the other hand, I still feel that the course is often unnecessarily brutal and could use better explanations.
[0] https://mitpress.mit.edu/books/probabilistic-graphical-model...
Also Bayesian Methods for Hackers: https://news.ycombinator.com/item?id=16482421
I envision more sophisticated AGI systems to use DNN or other NN techniques to learn about the world, and be able to take in uncertain input and make sense of it. PGM or similar would then be used to correctly (in the mathematical sense) reason about what to do to accomplish the agent's goals.
The lectures were OK, but the homework was more than challenging. Not only had you to battle with the topic itself, but then you need to magically acquire knowledge in some totally unknown topic (I think it was genetics) and wrangle the quite baroque representation of that topic shoehorned in a programming language that is totally not made for it.
I was on the verge of despair because of those secondary problems. Really.
It's good to hear that this MOOC is still so well thought of since I first took it; for me, it was the first course I took that made me really understand how a neural network and back prop actually worked.
When I took the course, it was in 2011 - and was known as "ML Class"; yep - I was among the first "beta tester guinea pigs" of the course. It was fun and amazing to participate in.
One of the early participants was even inspired to replicate CMU's ALVINN self-driving vehicle in miniature:
This course gave me professor envy and made me up my game. So well done.
Other music MOOCs I enjoyed:
The Berklee "Developing your Musicianship" series on Coursera taught by George W. Russell. Started off thinking this was too elementary, but the ear training is valuable, and I learned a lot about the use of diatonic chords, and even the few simple patterns he taught improved my song writing enormously.
The Berklee "Jazz Improvisation" class taught by Gary Burton. Very cool to be taught by a living legend, and his selection of songs was refreshingly modern. On the down side, skill levels of the students varied widely, so peer review was more miss than hit.
100% miss for me. The main thing I learned from that course is that a "MOOC" relying on peer review for feedback is a colossal waste of time, and should have stayed as a video lecture series.
Nicest: Andrew Ng, Machine Learning, Coursera. Interesting topic, well-planned material, very well avoids going into the mathy details, but still conveys a feeling of understanding of the topic, so accessible to a wide audience. (Martin Odersky, Functional Programming Principles in Scala, Coursera, was almost equally nice, but had some rough edges in the first run.)
Most interesting: Probabilistic Graphical Models, Daphne Koller, Coursera. Very interesting topic. I took the first run of the course and it had lots of rough edges. Needs a lot of work to apply the lectures to the homework. I haven't seen such a demanding course since I took quantum mechanics at university.
Best organized: Jennifer Widom, Databases, Stanford. This is not the flashiest of a topic, but oh boy was it well organized. Runs like a clockwork. Everything in the lectures is relevant, everything from the lectures is applied and tested in the homework, there is lots of homework (but still not enough to make you remember SQL,XPath,XQuery,XSLT for the rest of your life if you don't keep using them), weekly homework has a nice progression from simpler things to medium difficult things, and the web environment is well designed, and gives wonderful feedback and guides you to get your queries correct.