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 3. Working with Non-Parametric Models

In the last chapter, we introduced parametric models and explored how to implement linear and logistic regression. In this chapter, we will cover the non-parametric model family. We will start by covering the bias-variance trade-off, and explaining how parametric and non-parametric models differ at a fundamental level. Later, we'll get into decision trees and clustering methods. Finally, we'll address some of the pros and cons of the non-parametric models.

In this chapter, we will cover the following topics:

  • The bias/variance trade-off
  • An introduction to non-parametric models and decision trees
  • Decision trees
  • Implementing a decision tree from scratch
  • Various clustering methods
  • Implementing K-Nearest Neighbors (KNNs) from scratch
  • Non-parametric models – the pros and cons