There are many ways to implement a supervised learning system. The most popular of those is the neural network-- a brilliant algorithm that was loosely inspired by the connections of neurons in our own brains. Part II of this book is dedicated to neural networks. We'll grow the program from Part I into a full-fledged neural network. We'll have to overcome a few challenges along the way, but the payoff will be worth it: the final neural network will be way more powerful than the fledgling program we'll start off with.

Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.