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

Content-based filtering

In this section, we're going to wrap up our discussion around recommender systems by introducing an entirely separate approach to computing similarities and look at how we can use it to augment our collaborative filtering systems.


Content-based recommenders operate similarly to the original item-to-item collaborative system that we saw earlier, but they don't use ratings data to compute the similarities. Instead, they compute the similarities directly by using provided attributes of the items in the catalog. Predictions can then be computed in the same fashion as item-to-item collaborative filtering by calculating the product of the ratings matrix and similarity matrix.


Here's an example of how we might use content vectors to directly compute the item similarity matrix:

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

ratings = np.array(([5.0, 1.0, 0.0, 0.0, 2.5, 4.5, 0.0, 0.0],
                    [0.0, 0.0, 3.5, 2.0, 3.0, 0.0, 0.0, 0.0]...