Mastering NLP From Foundations to Agents

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