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 work with the relevant statistical measures and matrix techniques that will be utilized later in the book. That being said, this...
