Graph Machine Learning Essentials delivers a practical and technically grounded introduction to modern Graph ML, effectively connecting foundational graph concepts with real-world AI implementation workflows.
-- Lucas Cabral,
AI Engineer & Data Scientist
Graph Machine Learning Essentials is a compact, practical guide for engineers who want a quick start in Graph ML, covering key methods, tasks, applications, and implementation pathways. It is a valuable handbook for navigating modern graph ML concepts and real-world pipelines.
-- Dymitr Nowicki,
Ph.D. in Computer Science and Applied Mathematics,
Selecton Technologies Inc.
A comprehensive and accessible introduction to the burgeoning field of graph machine learning (GML). Aimed at readers with a basic understanding of machine learning, the book expertly balances theory and practice, making it suitable for students, professionals, and researchers alike.
The book begins by introducing graphs as structures that model relationships between entities, highlighting their ubiquity in domains like social networks, biology, and finance. Pintu explains why traditional machine learning methods fall short for graph-structured data, setting the stage for specialized techniques like node embeddings and Graph Neural Networks (GNNs). Each chapter builds logically on the last, covering core tasks (node classification, edge prediction, graph classification), advanced architectures, and practical considerations like scalability and over-smoothing.
What sets the book apart is its practical focus. Pintu includes code snippets, programming assignments, and discussions on real-world applications—such as fraud detection, recommender systems, and drug discovery—to ensure readers can apply what they learn. The use of quizzes and examples further reinforces understanding, while appendices on machine learning basics and PyTorch Geometric make the book self-contained.
Graph Machine Learning Essentials is an invaluable guide for anyone looking to understand and apply GML, offering both the theoretical foundations and the practical tools needed to harness the power of graph-structured data.
-- Wilson Yeung,
Reviewer
Machine Learning Essentials You Always Wanted to Know is a solid introduction to AI and ML, especially for beginners who already have a bit of "coding or technical background." What I liked most is how it keeps the curiosity alive throughout. It doesn’t go too deep into every topic, but it gives a good, broad overview, which I think is perfect for someone just starting out. The visualizations are really helpful and make the concepts easier to grasp. The overall tone stays engaging and encourages you to explore more. It's very beginner-friendly and keeps you wanting to learn more!
-- Akshat BahetiData Scientist, TD BankMachine Learning Essentials You Always Wanted to Know offers a clear, friendly, and practical introduction to machine learning. The book is structured like a guided learning journey—from understanding what machine learning is, to seeing how it’s applied in real life, to writing hands-on Python code. It’s beginner-friendly, yet technical enough to build a strong foundation. The historical timeline, real-world examples (like Netflix recommendations and Google Maps), and helpful visuals make the concepts relatable and easy to remember.
-- Julia AppelskogProductive Planet, Book Trade ProfessionalMachine Learning Essentialsoffers 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.
-- Eszter BoczanReviewer from UKParikh’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.
-- J. KromrieGoodreads ReviewerMachine 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 to machine learning.
-- Mark JohnsAmazon.com Reviewer
The author breaks down complex architectures into actionable insights, making it the perfect guide for both beginners and experts. A must-read for any professional working in the AI space looking to stay at the forefront of language model innovation.
-- Mani Garlapati, Sr. Technical Program Manager, Google
This works as an excellent textbook, moving up the ladder of complexity of concepts necessary for anybody wanting to be an AI Engineer. The real-life examples at the end of each section are a must read.
-- Kalpit Bhawalkar, Head of AI, Konverge AI
This book offers a systematic, instructor-ready framework that prepares students for meaningful AI use in professional settings. By grounding instruction in concrete examples, relevant conceptual distinctions, and applied decision-making frameworks, it avoids surface-level tool training and instead cultivates disciplined and principled thinking. The result is a learning experience that enables instructors to teach with intention and students to develop the practical, ethical competence required to embed AI responsibly into real business workflows.
-- Karl R. LaPan
Director, UF Innovate | Accelerate
The University of Florida
Some technology books feel like they are sprinting ahead, scattering jargon and assume you will keep up. This book doesn’t. It slows down. It feels like it was written by someone who remembers what it is like to be curious before being confident.There is no pressure to already understand AI. The author begins with the questions people usually ask–What is AI actually doing? Why does it matter? Where does human thinking end and machine learning begin? As a book lover, I appreciated that. I don’t want to be impressed by complexity; I want to be invited to understand it. The explanations are calm and clear. Concepts like machine learning and neural networks don’t feel like paths you are guided along. You are never made to feel behind for not knowing something already. Instead, understanding builds quietly, until words that once felt intimidating start to feel familiar.What stayed with me most is how human the book feels. AI isn’t treated as a cold, distant force but as something shaped by human choices, data and values. The reminder running through the book is simple and lasting; AI reflects us. This isn’t a book you rush through. I paused often, not from confusion, but from thought, noticing how deeply AI has already woven itself into daily life. That is what good books do, they follow you beyond the pages.It is not a technical manual, and it won’t turn you into an engineer or an expert. But if you want to understand before specializing, this book offers a steady, welcome foundation.By the end, I didn’t feel overwhelmed. I felt clearer, calmer and more curious. For a subject as vast and fast-moving as artificial intelligence, that is a quiet achievement.
-- Himsekha Rai, NetGalley Reviewer
This book is good for those interested in AI regardless of their level of understanding.
I learned the history, how the concept was developed. I learned about the vast amounts of data required and about the various ways of organizing and interpreting data for productive use. I appreciate the practical examples, such as how email programs identify spam and how visual recognition programs work. As the programs advanced, examples of how programs recognize and interpret human speech are given and how generative programs can create text and images.
I also learned that the programs can make mistakes with a few examples given. The latest programs available are listed with suggestions for use depending on what an individual wants to do with it. The ethical issues are also covered, giving examples of how the programs can be used to deceive people.
This is a very interesting book, much of it understandable by people not involved in programming.
-- Joan Nienhuis, Reviewer, Book Reviews from an Avid Reader
I really enjoyed Artificial Intelligence Essentials You Always Wanted to Know because it finally made AI feel understandable instead of overwhelming. The explanations of machine learning, deep learning, NLP, and generative AI are clear, well structured, and written for people who are curious rather than those already having technical expertise, which I appreciated so much. I loved the way the book balances theory with real-world applications and practical examples, plus the summaries and quizzes actually helped reinforce what I was learning instead of feeling like a filler. It’s the kind of guide that builds confidence as you read, and I finished it feeling informed, less intimidated, and genuinely excited about how AI fits into everyday life.
-- Marta Petticoat, NetGalley Reviewer
A concise yet comprehensive guide covering the full spectrum of modern AI, from core ML/DL to the latest in GenAI and ethics. Highly recommended for building foundational literacy.
-- Vinodh Balaraman, Co-founder & CEO, KolateAI Inc
