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Python Natural Language Processing Cookbook

You're reading from   Python Natural Language Processing Cookbook Over 60 recipes for building powerful NLP solutions using Python and LLM libraries

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781803245744
Length 312 pages
Edition 2nd Edition
Languages
Concepts
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Authors (2):
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Saurabh Chakravarty Saurabh Chakravarty
Author Profile Icon Saurabh Chakravarty
Saurabh Chakravarty
Zhenya Antić Zhenya Antić
Author Profile Icon Zhenya Antić
Zhenya Antić
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Learning NLP Basics 2. Chapter 2: Playing with Grammar FREE CHAPTER 3. Chapter 3: Representing Text – Capturing Semantics 4. Chapter 4: Classifying Texts 5. Chapter 5: Getting Started with Information Extraction 6. Chapter 6: Topic Modeling 7. Chapter 7: Visualizing Text Data 8. Chapter 8: Transformers and Their Applications 9. Chapter 9: Natural Language Understanding 10. Chapter 10: Generative AI and Large Language Models 11. Index 12. Other Books You May Enjoy

Who this book is for

Data scientists, machine learning engineers, and developers familiar with basic programming and data science concepts can gain practical insights from this book. It serves as a primer to introduce the concepts of NLP and their practical applications.

The roles that are the target of this book are the following:

Data scientists: As a data scientist, you will gain an understanding of how to work with text. Intermediate knowledge of Python will help you to get the most out of this book. If you are already an NLP practitioner, this book will serve as a code reference when working on your projects.

Software engineers and architects: Developers who want to build capability in the domain of NLP will be introduced to all the fundamental and advanced uses of NLP in text processing. This will help you level up on your knowledge and build yourself up to develop solutions using NLP when required.

Product managers: Though this book contains code examples for the recipes, each of these recipes is accompanied by explanations of why certain steps are being performed and what the end output for those steps is. This makes it a useful resource for product managers who want to understand what is possible with a certain NLP recipe, which would enable them to envision novel solutions using it.

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