Mastering NLP From Foundations to Agents - Second Edition

This second edition spans NLP foundations to LLMs, RAG, & agentic systems, teaching you to design and fine-tune production-ready AI solutions in Python.

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Key Features:

- Engineer NLP systems from ML foundations to LLM architectures

- Implement RAG pipelines, routing layers, and agent workflows

- Fine-tune and align LLMs using LoRA, RLHF, and DPO methods

- Design production-grade AI systems with governance and safety

Book Description:

Natural Language Processing has evolved beyond rule-based systems and classical machine learning (ML). This second edition guides you through that transformation from mathematical and ML foundations to large language models, retrieval pipelines, agentic automation, and AI-native system design. It strengthens core NLP concepts while expanding into modern architectures such as transformers, parameter-efficient fine-tuning (LoRA and QLoRA), and alignment methods like RLHF and DPO.

You'll begin with essential linear algebra, probability, and ML principles before moving into text preprocessing, feature engineering, classification pipelines, and deep learning architectures. From there, the focus shifts to system design: building Retrieval-Augmented Generation (RAG) pipelines, implementing model routing strategies that balance cost and performance, and orchestrating structured multi-agent workflows. You'll also introduce structured interoperability patterns, including the Model Context Protocol (MCP). Governance and safety will be treated as architectural concerns, demonstrating how policy and compliance can be integrated directly into AI systems. By the end, you will have the tools to implement NLP techniques and be equipped to design, govern, and deploy intelligent systems built on them.

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What You Will Learn:

- Build strong NLP foundations in math and ML

- Engineer text classification and NLP pipelines

- Train and fine-tune modern LLM architectures

- Implement RAG systems 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 with LoRA, QLoRA, and DPO techniques

Who this book is for:

This book is for machine learning engineers, data scientists, and NLP practitioners looking to deepen their expertise and build advanced AI 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

- An Introduction to the NLP Landscape

- Mathematical Foundations for Machine Learning in NLP

- Unleashing Machine Learning Potential in NLP

- Streamlining Text Preprocessing Techniques for Optimal NLP Performance

- Text Classification Using Traditional ML Techniques

- Text Classification Using Deep Learning Language Models

- Demystifying LLM Theory, Design, and Implementation

- Parameter-Efficient Fine-Tuning and Reasoning in LLMs

- Advanced Setup and Integration with RAG and MCP

- Advanced LLM Practices Using RAG and LangChain

- Multi-Agent Solutions and Advanced Agent Frameworks

- Technical Guardrails of AI Safety and Responsible Implementation

- Designing and Managing AI-Native Products