Mastering Text Analytics

This book is a comprehensive guide to mastering Natural Language Processing (NLP), a rapidly growing field in AI-powered text and data analytics. It equips you with tools and techniques to extract valuable insights from both structured and unstructured data, enabling you to uncover insights beyond the reach of traditional data analysis methods and stay competitive in this evolving domain.

The book starts with foundational concepts, such as collecting and extracting data for NLP projects, before progressing to advanced topics like applications of transfer learning in NLP and Large Language Models (LLMs). Each chapter emphasizes real-world applications and includes practical case studies to ensure the knowledge is immediately applicable. Throughout the book, readers will find Python code demonstrations, hands-on projects, and detailed explanations of key concepts. Special features include business use cases from industries like healthcare and customer service, practice exercises to reinforce learning, and explorations of emerging NLP technologies. These elements make the book not only informative but also highly engaging and interactive.

By the end of the book, the reader will have a solid foundation in Generative AI techniques to apply them to complex challenges. Whether you re a budding data scientist or a seasoned professional, this guide will help you harness the power of AI-driven text and data analytics effectively.

What you will learn:

• Understand NLP with easy-to-follow explanations, examples, and Python implementations.
• Explore techniques such as transformers, word embeddings, and pragmatic analysis in real-world contexts.
• Work with real-world datasets and apply pre-processing, tokenization, and text extraction using NLP libraries.
• How to build complete NLP pipelines from data collection to model implementation, including sentiment analysis and chatbots.
• Learn state-of-the-art methods like deep learning techniques in NLP, large language models (LLMs), and zero-shot learning in NLP.

Who this book is for:

This book is tailored for data scientists, machine learning engineers, AI practitioners, and software developers seeking to learn NLP techniques and apply them to solve problems.

Chapter 1. Natural Language Processing: An Introduction. - Chapter 2. Collecting and Extracting the Data for NLP Projects. - Chapter 3. NLP Data Preprocessing Tasks Involving Strings & Python Regular Expressions. - Chapter 4. NLP Data Preprocessing Tasks with nltk. - Chapter 5. Lexical Analysis. - Chapter 6. Syntactic and Semantic Techniques in NLP. - Chapter 7. Advanced Pragmatic Techniques and Specialized Topics in NLP. - Chapter 8. Transformers, Generative AI, & LangChain. - Chapter 9. Advancing with LangChain & OpenAI. - Chapter 10. Case Study on Symantec Analysis.