As we write the book Machine Learning in Practice (coming early in 2019), we'll be posting draft excerpts right here.
The article contains the best tutorial content that I've found so far. It's by no means an exhaustive list of every ML-related tutorial on the web - that would be overwhelming and duplicative. Plus, there is a bunch of mediocre content out there. My goal was to link to the best tutorials I found on the important subtopics within machine learning and NLP.
By tutorial, I'm referring to introductory content that is intending to teach a concept succinctly. I've avoided including chapters of books, which have a greater breadth of coverage, and research papers, which generally don't do a good job in teaching concepts. Why not just buy a book? Tutorials are helpful when you're trying to learn a specific niche topic or want to get different perspectives.
I've split this post into four sections: Machine Learning, NLP, Python, and Math. I've included a sampling of topics within each section, but given the vastness of the material, I can't possibly include every possible topic.
If there are good tutorials you are aware of that I'm missing, please let me know! I'm trying to limit each topic to five or six tutorials since much beyond that would be repetitive. Each link should have different material from the other links or present information in a different way (e.g. code versus slides versus long-form) or from a different perspective.
Activation and Loss Functions
Support Vector Machines
Optimization and Dimensionality Reduction
Long Short Term Memory (LSTM)
Convolutional Neural Networks (CNNs)
Recurrent Neural Nets (RNNs)
Generative Adversarial Networks (GANs)
Deep Learning and NLP
Scipy and numpy