Thanks to NetGalley and the publisher for the opportunity to preview this book.

From what I’ve seen, Machine Learning Essentials offers a clear, structured path into a field that can often feel intimidating. The layout is accessible and well-organised, with a step-by-step approach that eases readers into the fundamentals of machine learning. Even a quick glance reveals that it prioritises understanding over jargon and blends theory with practical examples - a combination I always appreciate in educational materials.

It seems like a valuable starting point for those curious about how ML works in real life - from everyday tech like recommendation engines to more advanced applications. I particularly liked the real-world analogies that help make complex ideas more digestible.

Based on the thoughtful structure and practical tone, I believe this book will be a helpful guide for anyone looking to get a solid grasp on machine learning - without being overwhelmed.

Thanks to the publisher and Netgalley for this eARC.

Parikh’s expertise as a data engineer and a technical writer shines through in his ability to make machine learning approachable. "Machine Learning Essentials You Always Wanted to Know" is a practical companion for anyone eager to understand and implement ML in meaningful ways. Whether you’re looking to enhance your career in AI or simply gain a deeper appreciation for the technology, this book will help you.

This book distills intricate ML principles into digestible explanations. Parikh avoids unnecessary jargon, opting instead for a structured, step-by-step approach that makes learning intuitive.

Unlike many theoretical ML books, "Machine Learning Essentials" bridges the gap between theory and real-world application. Parikh incorporates hands-on coding exercises, allowing readers to implement key algorithms and reinforce their understanding through practice.

The book covers essential ML topics, including supervised, unsupervised, and reinforcement learning, as well as key mathematical principles that underpin these techniques.

Parikh’s expertise as a data engineer and technical writer shines through in his ability to make machine learning approachable. 'Machine Learning Essentials You Always Wanted to Know" is a practical companion for anyone eager to understand and implement ML in meaningful ways.

"Machine Learning Essentials You Always Wanted to Know" is a concise, beginner-friendly guide that demystifies machine learning for students and professionals alike. The book stands out for its clear explanations and practical approach, covering foundational algorithms and concepts without overwhelming readers with math or jargon. It introduces core topics-such as supervised and unsupervised learning, key algorithms, and evaluation metrics-using real-world examples and hands-on coding exercises in Python, making it easy for newcomers to follow along.

Dhairya Parikh’s industry experience and academic background are evident in the book’s structure and clarity. The content is well-organized, starting from the basics and progressing to more advanced models, always emphasizing practical application. The inclusion of glossaries and quizzes at the end of each chapter supports self-paced learning.

As an IT executive, I appreciate how this book bridges theory and practice, making it an ideal resource for those looking to build foundational ML skills or transition into AI roles. While advanced practitioners may be looking for more depth, this book is an excellent starting point for anyone wanting a structured, understandable introduction into machine learning

I received a free copy of this book and am voluntarily leaving a review.

Clear, Friendly, and Practical—A Great Intro to Machine Learning
The book is structured like a guided learning journey—from what ML is, to how it's used in real life, to hands-on coding with Python.
It’s beginner-friendly, but still technical enough to give you a solid foundation.
The historical timeline, real-world examples (like Netflix recommendations and Google Maps), and visual aids make everything more relatable.