You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

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

3. Recognizing Faces with Support Vector Machine

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

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. 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. To make our journey more interesting, we will start with a natural language processing (NLP) problem— exploring newsgroups data. You will gain hands-on experience in working 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: