Chapter 1, First Step toward Supervised Learning, covers the basics of supervised machine learning to get you prepared to start tackling problems on your own. The chapter comprises four important sections. First, we will get our Anaconda environment set up and make sure that we are able to run the examples. Over the next couple of sections following that, we will cover a bit more of the theory behind machine learning, before we start implementing algorithms in the final section, where we'll get our Anaconda environment set up.
Chapter 2, Implementing Parametric Models, dives into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this section by formally introducing parametric models, then we'll focus on two very popular parametric models in particular: linear and logistic regression. We'll spend some time understanding the inner workings and then jump into Python and actually code them from scratch.
Chapter 3, Working with Non-Parametric Models, explores the non-parametric model family. We will start by covering the bias-variance trade-off, and explain how parametric and non-parametric models differ at a fundamental level. We will then get into decision trees and clustering methods. Finally, we'll address some of the pros and cons of non-parametric models.
Chapter 4, Advanced Topics in Supervised ML, splits its time between two topics: recommender systems and neural networks. We'll start with collaborative filtering and then talk about integrating content-based similarities into your collaborative filtering systems. Finally, we'll get into neural networks and transfer learning.
