Description
Master modern Natural Language Processing—from core machine learning concepts to Large Language Models, Retrieval-Augmented Generation (RAG), and intelligent AI agents.
This fully updated second edition provides a complete learning path through today’s NLP landscape, equipping you with the knowledge to design, fine-tune, and deploy production-ready AI applications using Python. Covering everything from mathematical fundamentals to cutting-edge LLM architectures, the book bridges traditional NLP techniques with the latest advances in generative AI.
Starting with essential concepts in linear algebra, probability, statistics, and machine learning, you’ll build a strong foundation before progressing to text preprocessing, feature engineering, classification models, and deep learning approaches. As your skills grow, you’ll explore transformer architectures, parameter-efficient fine-tuning techniques such as LoRA and QLoRA, and modern alignment methods including Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).
Beyond model development, the book emphasizes building complete AI systems. You’ll learn how to create Retrieval-Augmented Generation (RAG) pipelines, implement intelligent model-routing strategies that optimize performance and cost, and orchestrate sophisticated multi-agent workflows capable of solving complex tasks. It also introduces the Model Context Protocol (MCP) for seamless integration with external tools and data sources.
Special attention is given to responsible AI development, demonstrating how governance, safety, compliance, and policy enforcement can be incorporated directly into system architecture. By combining theoretical knowledge with practical implementation, you’ll gain the skills needed to build scalable, secure, and reliable AI-powered applications.
What You’ll Learn
- Build a strong foundation in NLP, mathematics, and machine learning
- Develop efficient text preprocessing and classification pipelines
- Train, fine-tune, and optimize transformer-based language models
- Build Retrieval-Augmented Generation (RAG) systems with LangChain
- Design and coordinate multi-agent AI workflows
- Evaluate model performance while implementing AI safety best practices
- Connect AI applications to external tools using the Model Context Protocol (MCP)
- Fine-tune large language models with LoRA, QLoRA, RLHF, and DPO techniques
- Design, deploy, and manage production-grade AI-native applications
Who This Book Is For
This book is ideal for machine learning engineers, data scientists, NLP practitioners, software developers, researchers, and AI enthusiasts who want to deepen their understanding of modern language technologies. It is particularly valuable for professionals building real-world AI solutions with LLMs and advanced NLP techniques. Readers should be comfortable with Python programming and have a basic understanding of machine learning concepts.
Topics Covered
- Introduction to the Modern NLP Ecosystem
- Mathematical Foundations for NLP and Machine Learning
- Machine Learning Techniques for NLP
- Text Preprocessing and Feature Engineering
- Traditional Machine Learning for Text Classification
- Deep Learning Approaches to Text Classification
- Large Language Model Architecture and Design
- Efficient Fine-Tuning and Reasoning Techniques
- Building RAG Pipelines and MCP Integrations
- Advanced LangChain Workflows
- Multi-Agent Systems and Agent Frameworks
- AI Safety, Governance, and Responsible Development
- Designing and Managing AI-Native Products
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