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Supervised Machine Learning with Python

By : Taylor Smith

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Buy this Book

Supervised Machine Learning with Python

5 (2)

By: Taylor Smith

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

First Step Towards Supervised Learning

Technical requirements

An example of supervised learning in action

Setting up the environment

Supervised learning

Hill climbing and loss functions

Hill climbing and descent

Model evaluation and data splitting

Summary

Implementing Parametric Models

Technical requirements

Parametric models

Implementing linear regression from scratch

Logistic regression models

Implementing logistic regression from scratch

The pros and cons of parametric models

Summary

Working with Non-Parametric Models

Technical requirements

The bias/variance trade-off

Introduction to non-parametric models and decision trees

Decision trees

Implementing a decision tree from scratch

Various clustering methods

Implementing KNNs from scratch

Non-parametric models – pros/cons

Summary

Advanced Topics in Supervised Machine Learning

Technical requirements

Recommended systems and an introduction to collaborative filtering

Matrix factorization

Matrix factorization in Python

Content-based filtering

Neural networks and deep learning

Neural networks

Using transfer learning

Summary

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Implementing Parametric Models

In the previous chapter, we got started with the basics of supervised machine learning. In this chapter, we will dive into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this chapter by formally introducing parametric models. Then, we'll introduce two very popular parametric models: linear and logistic regression. We'll spend some time looking at their inner workings and then we'll jump into Python and actually code those workings from scratch.

In this chapter, we will cover the following topics: