It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.
While it does talk through the basics of ML, it is really barely a taster. It doesn’t leave you with any skills, other than, if you buy a book and work through it, you will know what a “decision tree” is ahead of time.
With something like ML, the real value is in the deep nitty gritty, building intuition about methods you use, fighting the unfair battle against broken data etc, and all those things were missing to me.
At the birth of Coursera in 2012, R and Python were already clearly established in the field of data science. R was the dominant open-source language for data science, with Python very close behind (and already gaining ascendancy among folks who identified with "machine learning" rather than "data science"). I remember Matlab/Octave being more associated with academics/students in EE (signal processing, wireless communications, and the like); if you want clear insight into matrix operations, Octave is great.
I think Ng made a very conscious decision at the time to eschew built-in functions and not get distracted by trendy languages - hence the use of Octave to learn how to implement algorithms at the most basic linear-algebra level.
Even at the time his decision was not well understood nor popular - way back then I remember people asking "Why Octave instead of R or Python?"
In those classes that did use Matlab, there were quite a few people sticking to Octave, though it wasn't 100% compatable. And when I got to Ng's course, I (and I imagine a whole lot of others) were really happy to see he went 100% with Octave. Had he gone the Matlab route, the old course would be pretty worthless now.
We'll never know what happened behind closed doors, but I think Matlab was sponsoring some courses in order to get new people hooked on Matlab and it just didn't pay off.
I agree with a sibling comment that it was python and R were well established at the time.
Also makes you think about how comprehension is closely tied to expression even for ostensibly similar languages.
That said, I'm yet to see better coverage of that topics (If someone knows, I'd really like to get them. I forget pieces every now and then, and having more efficient refresh method is always welcome).
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6A...
instead of the one that started Coursera:
https://www.youtube.com/watch?v=PPLop4L2eGk&list=PLLssT5z_Ds...