Book Image

Supervised Machine Learning with Python

By : Taylor Smith
Book Image

Supervised Machine Learning with Python

By: Taylor Smith

Overview of this book

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.
Table of Contents (11 chapters)
Title Page
Copyright and Credits
About Packt

Chapter 4. Advanced Topics in Supervised Machine Learning

In this chapter, we're going to focus on some advanced topics. We'll cover two topics: recommender systems and neural networks. We'll start with collaborative filtering, and then we'll look at integrating content-based similarities into collaborative filtering systems. We'll get into neural networks and transfer learning. Finally, we'll introduce the math and concept behind each of these, before getting into Python code.

We will cover the following topics:

  • Recommended systems and an introduction to collaborative filtering
  • Matrix factorization
  • Content-based filtering
  • Neural networks and deep learning
  • Using transfer learning