Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found

While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate. In a recent article, I discussed why the broader artificial intelligence field is booming and likely will for some time to come. Those interested in learning ML may find it daunting to get started.

As I prepare to start my Ph.D. program in the Fall, I’ve been scouring the web for good resources on all aspects of machine learning and NLP. Typically, I’ll find an interesting tutorial or video, and that leads to three or four more tutorials or videos, and before I know it, I have 20 tabs of new material I need to go through. (On a side note, Tab Bundler has been helpful to stay organized.)

After finding over 25 ML-related “cheat sheets”, I created a post that links to all the good ones.

To help others that are going through a similar discovery process, I’ve put together a list of 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 LearningNLPPython, 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.

For future posts, I may create a similar list of books, online videos, and code repos as I’m compiling a growing collection of those resources too.

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.

https://unsupervisedmethods.com/over-150-of-the-best-machine-learning-nlp-and-python-tutorials-ive-found-ffce2939bd78

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