I've taken the first part of this (not the newest version using Pytorch, tho I'd like to retake someday soon), and found this class to be extremely useful. I do

I've taken the first part of this (not the newest version using Pytorch, tho I'd like to retake someday soon), and found this class to be extremely useful. I do agree that in order to be optimally efficient you'd want to have a background understanding of statistics/linear algebra; however, I love this class because it gets you up and running very quickly and excited about the content. It's also my understanding that the ML stack is making it more and more accessible to actually apply these algorithms. Sure, after you take this class you probably won't go out and invent new algorithmic breakthroughs, but many people in this class do participate in in Kaggle competitions solving real world problems (some of the assignments are submissions) and do very well, even winning occasionally. The point of the class is to generate interest and to get people useful quickly (make neural nets uncool again), it'd be much harder to get the field to blossom if you required people to start with years of stats/math beforehand.