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

Various clustering methods

In this section, we will cover the different clustering methods. First, let's look at what clustering is. Then we'll explain some of the mathematical tricks that we can use in clustering. And finally, we're going to introduce our newest non-parametric algorithm KNN.

What is clustering?

Clustering is about as intuitive as it gets in terms of machine learning models. The idea is we can segment groups of samples based on their nearness to one another. The hypothesis is the samples that are closer are more similar in some respects. So, there are two reasons we might want to cluster. The first is for discovery purposes, and we usually do this when we make no assumptions about the underlying structure of the data, or don't have labels. And so, this typically is done in a purely unsupervised sense. But as this is obviously a supervised learning book, we're going to focus on the second use case, which uses clustering as a classification technique.

Distance metrics

So, before...