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To be honest, this isn't the best list, it's a bit too blog heavy. I've started reading up on ML only recently but here are my recommendations. Note that I haven't went through all of them in entirety but they all seem useful. Note that a lot of them overlap to a large degree and that this list is more of a "choose your own adventure" than "you have to read all of these".


* Metacademy (http://metacademy.org) If you just want to check out what ML is about this is the best site.

* Better Explained (https://betterexplained.com/) if you need to brush up on some of the math

* Introduction to Probability (https://smile.amazon.com/Introduction-Probability-Chapman-St...)

* Stanford EE263: Introduction to Linear Dynamical Systems (http://ee263.stanford.edu/)


* Andrew Ng's class (http://cs229.stanford.edu)

* Python Machine Learning (https://smile.amazon.com/Python-Machine-Learning-Sebastian-R...)

* An Introduction to Statistical Learning (https://smile.amazon.com/Introduction-Statistical-Learning-A...)


* Pattern Recognition and Machine Learning (https://smile.amazon.com/Pattern-Recognition-Learning-Inform...)

* Machine Learning: A Probabilistic Perspective (https://smile.amazon.com/Machine-Learning-Probabilistic-Pers...)

* All of Statistics: A Concise Course in Statistical Inference (https://smile.amazon.com/gp/product/0387402721/)

* Elements of Statistical Learning: Data Mining, Inference, and Prediction (https://smile.amazon.com/gp/product/0387848576(

* Stanford CS131 Computer vision (http://vision.stanford.edu/teaching/cs131_fall1617/)

* Stanford CS231n Convolutional Neural Networks for Visual Recognition (http://cs231n.github.io/)

* Convex Optimization (https://smile.amazon.com/Convex-Optimization-Stephen-Boyd/dp...)

* Deep Learning (http://www.deeplearningbook.org/ or https://smile.amazon.com/Deep-Learning-Adaptive-Computation-...)

* Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/)


* Probabilistic Graphical Models: Principles and Techniques (https://smile.amazon.com/Probabilistic-Graphical-Models-Prin...)

I have also found that looking into probabilistic programming is helpful too. These resources are pretty good:

* The Design and Implementation of Probabilistic Programming Languages (http://dippl.org)

* Practical Probabilistic Programming (https://smile.amazon.com/Practical-Probabilistic-Programming...)

The currently most popular ML frameworks are scikit-learn, Tensorflow, Theano and Keras.

For someone who has basic background knowledge in ML and wants to know more about NN and DL, my list would be:

* Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) - perfect overview, go over it twice at least (the second time you will understand much of the decisions in the start)

* Tensorflow and deep learning, without a PhD (https://www.youtube.com/watch?v=sEciSlAClL8) - as much as I hate video lectures, this one was worth it; a good complement to the book above

* Theano Tutorial (http://deeplearning.net/software/theano/tutorial/index.html) - using Theano or TensorFlow takes some getting used to. I found TensorFlow documentation absolutely horrible for beginners, probably because the authors expect users to already know such frameworks. Once you learn Theano you won't have trouble with TensorFlow (if that's what you want to use).

Then there are more specific papers, but I guess those depend on the problem at hand.

Thanks for putting the list together.

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