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

Matrix factorization

In this section, we're going to look into recommender systems and introduce matrix factorization techniques. In typical collaborative filtering problems, we have users along one axis and items or offers along the other axis. We want to solve for the predicted rating for a user for any given item, but to get there we have to somehow compute the affinity between the users or the item. In the previous section, we looked at item-to-item collaborative filtering, where we explicitly computed the similarity matrix using the cosine similarity metric, but now we want to explore a method that's not going to explicitly compare items to items or users to users.

Matrix factorization is a form of collaborative filtering that focuses on the intangibles of products. At a conceptual level, every product or restaurant, for example, has intangibles that cause you to like, dislike, or remain indifferent toward them. For example, for a restaurant, maybe the atmosphere or the vibe you get...