ML from the Fundamentals

This series clearly explains machine learning in a "from the first principles" style. I did not assume the reader has any experience with machine learning so anyone should be able to follow it. Each does post assume you are familiar with the basics of linear algebra and calculus though. I could try to explain all math, but 3blue1brown would do a better job anyway so just watch his videos.

This series has a corresponding GitHub repository which you can find here. While reading the posts, you should also run the notebook to see how the theory works in a real world problem. It's the only way to really learn how everything works.

I publish a new blog post every Saturday. Be sure to follow me on Twitter so you stay up to date with the series. I'm @rickwierenga.

Chapters (WIP)

  1. Polynomial Regression from Scratch in Python
  2. Logistic Regression from Scratch in Python
  3. Softmax Regression from Scratch in Python
  4. An Intuitive Guide to Neural Networks


These are some resources I used to learn machine learning that you might like as well: