Mastering NLP From Foundations to Agents: Building AI Agents through Agentic Automation and RAG Workflows with Python 2nd ed. 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.Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesEngineer NLP systems from ML foundations to LLM architecturesImplement RAG pipelines, routing layers, and agent workflowsFine-tune and align LLMs using LoRA, RLHF, and DPO methodsDesign production-grade AI systems with governance and safetyBook DescriptionNatural 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.*Email sign-up and proof of purchase requiredWhat you will learnBuild strong NLP foundations in math and MLEngineer text classification and NLP pipelinesTrain and fine-tune modern LLM architecturesImplement RAG systems with LangChainOrchestrate multiple AI agents and tools to solve complex tasksEvaluate NLP model performance and apply AI safety best practicesIntegrate external data and tools using Model Context Protocol (MCP)Fine-tune transformers with LoRA, QLoRA, and DPO techniquesWho this book is forThis 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 ContentsAn Introduction to the NLP LandscapeMathematical Foundations for Machine Learning in NLPUnleashing Machine Learning Potential in NLPStreamlining Text Preprocessing Techniques for NLPText Classification Using Traditional ML TechniquesText Classification Part 2 - Using Deep Learning Language ModelsDemystifying LLM Theory, Design, and ImplementationParameter-Efficient Fine-Tuning and Reasoning in LLMsAdvanced Setup and Integration with RAG and MCPAdvanced LLM Practices Using RAG and LangChainMulti-Agent Solutions and Advanced Agent FrameworksTechnical Guardrails of AI Safety and Responsible ImplementationDesigning and Managing AI-Native Products Read more