Chapter 1, Navigating the NLP Landscape: A Comprehensive Introduction, explains what the book is about, which topics we will cover, and who can use this book. This chapter will help you decide whether this book is the right fit for you or not.
Chapter 2, Mastering Linear Algebra, Probability, and Statistics for Machine Learning and NLP, has three parts. In the first part, we will review the basics of linear algebra that are needed at different parts of the book. In the next part, we will review the basics of statistics, and finally, we will present basic statistical estimators.
Chapter 3, Unleashing Machine Learning Potentials in NLP, discusses different concepts and methods in ML that can be used to tackle NLP problems. We will discuss general feature selection and classification techniques. We will cover general aspects of ML problems, such as train/test/validation selection, and dealing with imbalanced datasets. We will also discuss performance metrics for evaluating ML models that are used in NLP problems. We will explain the theory behind the methods as well as how to use them in code.
Chapter 4, Streamlining Text Preprocessing Techniques for Optimal NLP Performance, talks about various text preprocessing steps in the context of real-world problems. We will explain which steps suit which needs, based on the scenario that is to be solved. There will be a complete Python pipeline presented and reviewed in this chapter.
Chapter 5, Empowering Text Classification: Leveraging Traditional Machine Learning Techniques, explains how to perform text classification. Theory and implementation will also be explained. A comprehensive Python notebook will be covered as a case study.
Chapter 6, Text Classification Reimagined: Delving Deep into Deep Learning Language Models, covers the problems that can be solved using deep learning neural networks. The different problems in this category will be introduced to you so you can learn how to efficiently solve them. The theory of the methods will be explained here and a comprehensive Python notebook will be covered as a case study.
Chapter 7, Demystifying Large Language Models: Theory, Design, and Langchain Implementation, outlines the motivations behind the development and usage of LLMs, alongside the challenges faced during their creation. Through an examination of state-of-the-art model designs, you will gain comprehensive insights into the theoretical underpinnings and practical applications of LLMs.
Chapter 8, Accessing the Power of Large Language Models: Advanced Setup and Integration with RAG, guides you through setting up LLM applications, both API-based and open source, and delves into prompt engineering and RAGs via LangChain. We will review practical applications in code.
Chapter 9, Exploring the Frontiers: Advanced Applications and Innovations Driven by LLMs, dives into enhancing LLM performance using RAG, exploring advanced methodologies, automatic web source retrieval, prompt compression, API-cost reduction, and collaborative multi-agent LLM teams, pushing the boundaries of current LLM applications. Here, you will review multiple Python notebooks, each handling different advanced solutions to practical use cases.
Chapter 10, Riding the Wave: Analyzing Past, Present, and Future Trends Shaped by LLMs and AI, dives into the transformative impact of LLMs and AI on technology, culture, and society, exploring key trends, computational advancements, the significance of large datasets, and the evolution, purpose, and social implications of LLMs in business and beyond.
Chapter 11, Exclusive Industry Insights: Perspectives and Predictions from World Class Experts, offers a deep dive into future NLP and LLM trends through conversations with experts in legal, research, and executive roles, exploring challenges, opportunities, and the intersection of LLMs with professional practices and ethical considerations.
