You're reading from Mastering NLP from Foundations to LLMs Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781804619186

Length 340 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (2):

Meysam Ghaffari

Lior Gazit

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Table of Contents (14) Chapters

Preface

1. Chapter 1: Navigating the NLP Landscape: A Comprehensive Introduction

2. Chapter 2: Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP FREE CHAPTER

3. Chapter 3: Unleashing Machine Learning Potentials in Natural Language Processing

4. Chapter 4: Streamlining Text Preprocessing Techniques for Optimal NLP Performance

5. Chapter 5: Empowering Text Classification: Leveraging Traditional Machine Learning Techniques

6. Chapter 6: Text Classification Reimagined: Delving Deep into Deep Learning Language Models

7. Chapter 7: Demystifying Large Language Models: Theory, Design, and Langchain Implementation

8. Chapter 8: Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG

9. Chapter 9: Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs

10. Chapter 10: Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI

11. Chapter 11: Exclusive Industry Insights: Perspectives and Predictions from World Class Experts

12. Index

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Technical requirements

To follow along with the examples and exercises in this chapter on text preprocessing, you will need a working knowledge of a programming language such as Python, as well as some familiarity with NLP concepts. You will also need to have certain libraries installed, such as Natural Language Toolkit (NLTK), spaCy, and scikit-learn. These libraries provide powerful tools for text preprocessing and feature extraction. It is recommended that you have access to a Jupyter Notebook environment or another interactive coding environment to facilitate experimentation and exploration. Additionally, having a sample dataset to work with can help you understand the various techniques and their effects on text data.

Text normalization is the process of transforming text into a standard form to ensure consistency and reduce variations. Different techniques are used for normalizing text, including lowercasing, removing special characters, spell checking, and stemming or lemmatization...