Need advice about how to divide this roadmap into some groups.

[ZuzooVn]

From the near future machine learning developer's point of view, this roadmap can be divided into some groups: Newbie, Explorer, Adventurer, Trailblazer

At the moment, I think all of the resources here are belong to "Newbie".
Currently, I have no ideas about A little bit more advanced roadway with math and theory.

Pull requests are always welcome.

[ZuzooVn]

I received comments from Hacker News's user adamnemecek (He's also on Github: @adamnemecek) for this roadmap.

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".
Reqs:

• 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/)
  Beginner:
• 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...)
  Intermediate:
• 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/)
  Advanced:
• 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.