[NelsonMinar]

I thought it was useful but awfully low level. For example I hope to never, ever implement backpropagation again; I'm going to use whatever code is in TensorFlow or PyTorch or whatever. But as a student I'm glad I did implement it myself, once, so I understand what is going on. More broadly it demystifies the black box of machine learning methods and you can see it for the giant pile of statistical categorizing functions that it is.

The most practical takeaway I got from Ng's course was the dangers of under and overfitting your data and techniques for detecting when you make that mistake.

[ghaff]

I still remember a talk by a woman from Google at a fairly long ago now O'Reilly conference (R.I.P). Part of what she discussed was Research AI vs. Applied AI. The gist of it was that a lot of the things in university course, graduate programs, etc. are tilted towards Research AI and you can get away without a lot of that stuff by using pre-built tooling for practical machine learning applications.

Of course, you want to have some understanding of what's going on under the covers but, for a lot of people, starting from first principles is quite hard and isn't really necessary.

[vasili111]

Is the knowing only Algebra I enough for this course?

[NelsonMinar]

Not the course I took. It relies on basic linear algebra like matrix multiplication. You can probably get through it with just coding and not understanding the math but it wouldn't be much fun.

Not sure about the new course.