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Machine Learning Techniques for Text

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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

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 12. Other Books You May Enjoy

To get the most out of this book

You will need a version of Python installed on your computer—the latest version, if possible. All code examples have been tested using Python 3.10 on Windows. However, they should work with future version releases too.

Software/hardware covered in the book

Operating system requirements

Python 3.10

Windows, macOS, or Linux

Microsoft C++ Build Tools

Windows

The Python examples in the book are available as Jupyter notebooks, and you need to use an IDE such as Visual Studio Code (https://code.visualstudio.com/) to run them. You also need a Gmail account to download specific resources.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

In certain notebooks, the code uses reduced versions of the datasets to limit the run time to an acceptable level. Feel free to adjust the size of the datasets based on your system configuration. At the end of each chapter, you are strongly urged to re-execute the code by alternating the configuration of each machine learning algorithm.

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