You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Predicting Online Ad Click-Through with Tree-Based Algorithms

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

8. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Recognizing Faces with Support Vector Machine

In the previous chapter, we discovered underlying topics using clustering and topic modeling techniques. This chapter continues our journey of supervised learning and classification, with a particular emphasis on Support Vector Machine (SVM) classifiers.

SVM is one of the most popular algorithms when it comes to high-dimensional spaces. The goal of the algorithm is to find a decision boundary in order to separate data from different classes. We will discuss in detail how that works. Also, we will implement the algorithm with scikit-learn and apply it to solve various real-life problems, including our main project of face recognition. A dimensionality reduction technique called principal component analysis, which boosts the performance of the image classifier, will also be covered in this chapter, as will support vector regression.

This chapter explores the following topics: