You're reading from Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803242385

Length 448 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Nikos Tsourakis

View More author details

Table of Contents (13) Chapters

Preface

1. Chapter 1: Introducing Machine Learning for Text

2. Chapter 2: Detecting Spam Emails FREE CHAPTER

3. Chapter 3: Classifying Topics of Newsgroup Posts

4. Chapter 4: Extracting Sentiments from Product Reviews

5. Chapter 5: Recommending Music Titles

6. Chapter 6: Teaching Machines to Translate

7. Chapter 7: Summarizing Wikipedia Articles

8. Chapter 8: Detecting Hateful and Offensive Language

9. Chapter 9: Generating Text in Chatbots

10. Chapter 10: Clustering Speech-to-Text Transcriptions

11. Index

Why subscribe?

12. Other Books You May Enjoy

Technical requirements

The code of this chapter is available as a Jupyter Notebook in the book’s GitHub repository: https://github.com/PacktPublishing/Machine-Learning-Techniques-for-Text/tree/main/chapter-02.

The Notebook has an in-built step to download the necessary Python modules required for the practical exercises in this chapter. Furthermore, for Windows, you need to download and install Microsoft C++ Build Tools from the following link: https://visualstudio.microsoft.com/visual-cpp-build-tools/.

You're reading from Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

Table of Contents (13) Chapters

Technical requirements

Authors (1)