I'm linking to a free online book I just finished called Machine Learning from Scratch. The book aims to cover the complete, technical, "under the hood" details that other ML textbooks don't. To do that, it shows all the mathematical steps to derive common algorithms and demonstrates how to fit each one from scratch in Python (using just numpy).

You might like this book if you are interested in ML and like to really understand what's going on behind the algorithms. Personally, when learning about these algorithms I would understand them intuitively but not feel comfortable constructing them myself. Even if you totally grasp the algorithms, seeing them derived from scratch is a great way to better understand the comparative advantages between competing models.

The code and math are pretty simple, so there should be a low barrier to entry.

I'm hoping to make the book somewhat of a breathing document, so if you have any edits, questions, or suggestions, I'm all ears. You can reach me at dafrdman@gmail.com. Thanks a lot!