Data Science with Generative AI - Volume 5: Generative AI

Volume 5 — Generative AI

Chapters 45–64. The centre of the whole series. You'll learn how large language models actually work, how to call them reliably from Python, and how to prompt them well — then put them to work on real data: natural-language analytics, embeddings, vector databases, and retrieval-augmented generation, from prototype to advanced technique. From there it moves into AI agents — built from scratch, with the major frameworks, and coordinated in multi-agent teams — plus fine-tuning, open-source models, multimodal AI, and how to evaluate an LLM system rigorously instead of just eyeballing it.

Who it's for: anyone who wants to go straight at LLMs, RAG, and agents — software engineers adding GenAI to what they build, and data scientists moving from classical ML into generative systems.

What you get: PDF + EPUB of Volume 5. (Book + Resources adds runnable code files/notebooks for every chapter; Full Bundle adds the Start Here reading-path guide, plus the Prompt Library pulled from this volume's prompting examples.)

At 341 pages across 20 chapters, this is the volume most likely to need a periodic refresh as the ecosystem moves — a living Errata & Updates page (included in the Full Bundle) tracks exactly which library versions are still current.