First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7. If you don't understand it, keep reading it until you do. You can read the rest of the

First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7. If you don't understand it, keep reading it until you do. You can read the rest of the book if you want. You probably should, but I'll assume you know all of it. Take Andrew Ng's Coursera. Do all the exercises in Matlab and python and R. Make sure you get the same answers with all of them. Now forget all of that and read the deep learning book. Put tensorflow or torch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed forward NNs. Once you do all of that, go on arXiv and read the most recent useful papers. The literature changes every few months, so keep up. There. Now you can probably be hired most places. If you need resume filler, so some Kaggle competitions. If you have debugging questions, use StackOverflow. If you have math questions, read more. If you have life questions, I have no idea. source: fizixer https://news.ycombinator.com/item?id=13890952 https://news.ycombinator.com/item?id=13890952 FWIW, a "super harsh" guide to (learning) ML [1] was posted on reddit a few days ago. [1] https://redd.it/5z8110 https://redd.it/5z8110 Edit: The entire Reddit discussion feels slightly similar to this one, if more snarky. The first reply there also links all the resources listed above. I don't really know enough to add anything.