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My Favorite AI & ML Books That Shaped My Learning Over the years, I’ve read tons of books in AI, ML, and LLMs — but these are the ones that stuck with me the most. Each book on this list taught me something new about building, scaling, and understanding intelligent systems. Here’s my curated list — with one-line summaries to help you pick your next read: ⛳ Machine Learning & Deep Learning 1.Hands-On Machine Learning ↳Beginner-friendly guide with real-world ML & DL projects using Scikit-learn, Keras, and TensorFlow. ↳ https://amzn.to/42jvdok 2.Understanding Deep Learning ↳A clean, intuitive intro to deep learning that balances math, code, and clarity. ↳ https://amzn.to/4lEvqd8 3.Deep Learning ↳A foundational deep dive into the theory and applications of DL, by Goodfellow et al. ↳ https://amzn.to/3GdhmqU 🧠 LLMs, NLP & Prompt Engineering 4.Hands-On Large Language Models ↳Build real-world LLM apps — from search to summarization — with pretrained models. ↳ https://amzn.to/4jENXV4 5.LLM Engineer’s Handbook ↳End-to-end guide to fine-tuning and scaling LLMs using MLOps best practices. ↳ https://amzn.to/4jDEfCn 6.LLMs in Production ↳Real-world playbook for deploying, scaling, and evaluating LLMs in production environments. ↳ https://amzn.to/42DiBHE 7.Prompt Engineering for LLMs ↳Master prompt crafting techniques to get precise, controllable outputs from LLMs. ↳ https://amzn.to/4cIrbcP 8.Prompt Engineering for Generative AI ↳Hands-on guide to prompting both LLMs and diffusion models effectively. ↳ https://amzn.to/4jDEjSD 9.Natural Language Processing with Transformers ↳Use Hugging Face transformers for NLP tasks — from fine-tuning to deployment. ↳ https://amzn.to/43VaQyZ 🎨 Generative AI 10.Generative Deep Learning ↳Train and understand models like GANs, VAEs, and Transformers to generate realistic content. ↳ https://amzn.to/4jKVulr 11.Hands-On Generative AI with Transformers and Diffusion Models ↳Create with AI across text, images, and audio using cutting-edge generative models. ↳ https://amzn.to/42tqVcE 🛠️ ML Systems & AI Engineering 12.Designing Machine Learning Systems ↳Blueprint for building scalable, production-ready ML pipelines and architectures. ↳ https://amzn.to/4jGDQ25 13.AI Engineering ↳Build real-world AI products using foundation models + MLOps with a product mindset. ↳ https://amzn.to/4lDQ5ya These books helped me evolve from writing models in notebooks to thinking end-to-end — from prototyping to production. Hope this helps you wherever you are in your journey. ✓ Would love to hear what books shaped your AI path — drop your favorites below⬇️