Mastering NLP From Foundations to Agents

About the Book

Stay ahead in NLP by mastering core skills and cutting-edge techniques. This fully updated second edition teaches you to build powerful language solutions using the latest LLMs, RAG, and AI agents Key Features Build autonomous AI agents by orchestrating LLMs and tools with frameworks such as LangChain Use updated Python code and modern libraries (e.g., LoRA) to implement advanced NLP techniques Design technical guardrails for safe and responsible use of LLMs and AI agents Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionKeeping up with the rapid advancements in NLP can be challenging. Mastering NLP from Foundations to Agents, Second Edition is a complete guide to navigating this evolving landscape. Written by NLP experts, this updated edition not only reinforces core NLP and Machine Learning (ML) fundamentals but also teaches you the latest techniques to build cutting-edge language applications. It offers fully revised content with new chapters on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agent architectures, model evaluation, and AI safety—ensuring you stay at the forefront of modern NLP. You'll begin with essential math and ML foundations, then move on to text preprocessing and classic NLP tasks. With these fundamentals in place, the book delves into advanced topics: you’ll learn to integrate large language models (LLMs) into your applications, implement RAGS, and even orchestrate multiple AI agents using frameworks like LangChain. This edition includes updated Python examples (provided as Jupyter notebooks on GitHub) that leverage the latest libraries, including techniques like LoRA for efficient LLM fine-tuning. By the end of the book, you’ll be able to confidently build advanced NLP solutions that combine solid fundamentals with the power of LLMs and AI agentsWhat you will learn Master the core math and Machine Learning foundations of NLP Build and train text classification and other NLP models in Python Fine-tune Large Language Models (LLMs) for real-world NLP tasks Implement Retrieval-Augmented Generations (RAGs) with LangChain Orchestrate multiple AI agents and tools to solve complex tasks Evaluate NLP model performance and apply AI safety best practices Integrate external data and tools using Model Context Protocol (MCP) Fine-tune transformers efficiently with LoRA, QLoRA, and DPO techniques Who this book is forThis book is for machine learning engineers, data scientists, and NLP practitioners looking to deepen their expertise and build advanced language solutions. It also benefits professionals and researchers who want to apply the latest NLP and LLM techniques in real-world projects. Software engineers entering the AI field and tech enthusiasts keen on modern NLP advancements will find it valuable. A solid understanding of Python and basic Machine Learning concepts is assumed

Table of Contents:
Table of Contents

1. Navigating the NLP Landscape – A Comprehensive Introduction
2. Mastering Linear Algebra, Probability, and Statistics for ML and NLP
3. Unleashing Machine Learning Potentials in NLP
4. Streamlining Text Preprocessing Techniques for Optimal NLP Performance
5. Empowering Text Classification – Leveraging Traditional ML Techniques
6. Text Classification Reimagined – Deep Learning & Transformer Models
7. Demystifying LLMs – Theory, Design, and Implementation
8. TBD Dedicated to advanced topics: fine tuning, RLHF, reasoning)
9. Advanced Setup and Integration: with RAGs and MCP
10. Multi-Agent Solutions & Advanced Agent Frameworks
11. Product Information
12. TBD Technical Guardrails: The Architecture of AI Safety and Responsible Implementation
13. Including industry trends and Exclusive Industry Insights

About the Author :
Lior Gazit is a highly skilled Machine Learning professional with a proven track record of success in building and leading teams drive business growth. He is an expert in Natural Language Processing and has successfully developed innovative Machine Learning pipelines and products. He holds a Master degree and has published in peer-reviewed journals and conferences. As a Senior Director of the Machine Learning group in the Financial sector, and a Principal Machine Learning Advisor at an emerging startup, Lior is a respected leader in the industry, with a wealth of knowledge and experience to share. With much passion and inspiration, Lior is dedicated to using Machine Learning to drive positive change and growth in his organizations. Meysam Ghaffari is a Senior Data Scientist with a strong background in Natural Language Processing and Deep Learning. Currently working at MSKCC, where he specialize in developing and improving Machine Learning and NLP models for healthcare problems. He has over 9 years of experience in Machine Learning and over 4 years of experience in NLP and Deep Learning. He received his Ph.D. in Computer Science from Florida State University, His MS in Computer Science - Artificial Intelligence from Isfahan University of Technology and his B.S. in Computer Science at Iran University of Science and Technology. He also worked as a post doctoral research associate at University of Wisconsin-Madison before joining MSKCC.