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

Recommended systems and an introduction to collaborative filtering

In this section, we'll cover collaborative filtering and recommender systems. We'll start out by explaining what may constitute a recommender system, how users willingly share loads of data about themselves, without knowing it, and then we'll cover collaborative filtering.

Whether you realize it or not, you interact with numerous recommender systems on a daily basis. If you've ever purchased from Amazon, or browsed on Facebook, or watched a show on Netflix, you've been served some form of personalized content. This is how e-commerce platforms maximize conversion rates and keep you coming back for more.

One of the marks of a really good recommender system is that it knows what you want whether you already know it or not. A good one will make you really wonder: how did they know that? So, it turns out that humans are extraordinarily predictable in their behavior, even without having to share information about themselves, and...