I’ve been in this industry for years, from building classic recommendation engines to fine-tuning massive language models. And I can tell you the single biggest shortcut isn’t another 10-hour video course. It’s a well-chosen book. A book gives you something tutorials can’t: a structured, coherent, and deep understanding from a single, expert voice. So, in this article, I’ll take you through some popular e-books for all AI/ML Beginners.
1. From ML Algorithms to GenAI & LLMs
Full disclosure, this one is mine. I wrote it to solve a problem I see constantly: the massive gap between learning classic ML and understanding the Generative AI and LLM revolution happening right now. The other books provide an incredible foundation, but the field has evolved at lightning speed.
My book is the bridge. I wrote this from my notes, filled with the shortcuts, mental models, and practical advice I share with my junior engineers to get them up to speed fast. It’s designed to be the most direct path from being an ML beginner to a GenAI-aware practitioner.
If you are a beginner who has finished the foundational Data Science concepts, or professionals who feel their ML knowledge is a bit dated, this book is definitely for you. Find it here.
2. Deep Learning with Python by François Chollet
François Chollet created Keras, one of the most popular and intuitive deep learning libraries in the world. Learning from him is like learning to paint from Monet. He doesn’t just teach you the code; he teaches you the philosophy and the art of building neural networks.
This book is less about math and more about intuition. You’ll learn to think about neural networks as tools for information transformation and representation. He explains concepts like embeddings, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) with such clarity that you’ll feel like you could explain them to your grandma.
If you are someone who has a basic grasp of machine learning and wants to dive deep into neural networks, this book is definitely for you. Find it here.
3. Practical Statistics for Data Scientists by Peter Bruce, Andrew Bruce & Peter Gedeck
You can build a thousand models, but if you can’t validate them or understand the uncertainty in your results, you’re just guessing. This book is a lifesaver. It’s not your dreaded college statistics course. It’s a practical guide to the statistical concepts that actually matter on the job.
It covers A/B testing, regression analysis, and statistical significance in the context of real-world business problems. After reading this, you won’t just be a model-builder; you’ll be a scientific thinker who can use data to make credible, defensible decisions. It’s the skill that separates junior engineers from senior leaders.
This book is actually for everyone. Seriously. Whether you’re a data analyst, ML engineer, or data scientist, a solid grasp of practical statistics is non-negotiable. Find it here.
Final Words
You don’t need to read all of these at once. Pick one. The one that speaks to the biggest gap in your knowledge right now. The goal isn’t to become an expert overnight. The goal is to be a little bit smarter and more capable than you were yesterday. These books aren’t just collections of information; they are condensed mentorship.
I hope you liked this article on some popular e-books for all AI/ML Beginners. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
