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

Mining the 20 Newsgroups Dataset with Text Analysis Techniques

In previous chapters, we went through a bunch of fundamental machine learning concepts and supervised learning algorithms. Starting from this chapter, as the second step of our learning journey, we will be covering in detail several important unsupervised learning algorithms and techniques related to text analysis. To make our journey more interesting, we will start with a Natural Language Processing (NLP) problem—exploring the 20 newsgroups data. You will gain hands-on experience and learn how to work with text data, especially how to convert words and phrases into machine-readable values and how to clean up words with little meaning. We will also visualize text data by mapping it into a two-dimensional space in an unsupervised learning manner.

We will go into detail on each of the following topics: