Although we will take a mostly practical/applied approach to machine learning throughout this book, certain fundamental topics are essential to understand and properly apply machine learning. In particular, a fundamental understanding of probability and statistics will allow us to match certain algorithms with relevant problems, understand our data and results, and apply necessary transformations to our data. Matrices and a little linear algebra will then allow us to properly represent our data and implement optimizations, minimizations, and matrix-based transformations.
Do not worry too much if you are a little rusty in math or statistics. We will cover a few of the basics here and show you how to programmatically ...
