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GNNs, PyTorch Geometric, Supervised, Unsupervised, and Deep Learning Algorithms — Set of 2 Books
Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
Estimated delivery between August 15 and August 17.
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Graph Machine Learning & Machine Learning Essentials You Always Wanted to Know
GNNs, PyTorch Geometric, Supervised, Unsupervised, and Deep Learning Algorithms — Set of 2 Books
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Build a Strong Foundation in Modern Machine Learning
Build a strong foundation in modern machine learning with this practical 2-book collection—whether you're just starting out or looking to deepen your technical expertise in AI.
Book 1 – Graph Machine Learning Essentials
- Understand graph fundamentals, node embeddings, and message passing from basic concepts to practical implementation
- Build Graph Neural Networks with PyTorch Geometric and learn about scalability, oversmoothing, and real-world challenges
- See how graph machine learning applies to social networks, fraud detection, recommender systems, bioinformatics, and knowledge graphs
Book 2 – Machine Learning Essentials You Always Wanted to Know
- Discover what machine learning is, how it evolved, and where it shows up in everyday products
- Learn the ML workflow—data, models, training, and evaluation—with beginner-friendly explanations
- Explore supervised and unsupervised learning, plus an introduction to deep learning
Together, these books equip you with both foundational knowledge and specialized skills in graph-based AI, positioning you to advance your career in machine learning, data science, and artificial intelligence.
Pages: 476 pages
Paperback (ISBN): 9781636517766
Category: Business & Economics
Author: Dhairya Parikh, Pintu Kumar, Vibrant Publishers
Click on individual book titles below to view their complete Table of Contents.
Dhairya Parikh is a seasoned data engineer, a graduate of the University of Waterloo, and a technical writer with expertise in AI, data science, and practical ML applications.
Pintu Kumar is a Ph.D. scholar at IIT Bombay specializing in graph machine learning. A PMRF fellow and Silver Medalist in Mathematics, he focuses on research, teaching, and making complex ideas accessible.
Vibrant Publishers is focused on presenting the best texts for learning about technology and business as well as books for test preparation. Categories include programming, operating systems and other texts focused on IT. In addition, a series of books helps professionals in their own disciplines learn the business skills needed in their professional growth.
Vibrant Publishers has a standardized test preparation series covering the GMAT, GRE and SAT, providing ample study and practice material in a simple and well organized format, helping students get closer to their dream universities.
The Self-Learning Management Series is designed to help students, new managers, career switchers, and entrepreneurs learn essential management lessons and covers every aspect of business, from HR to Finance to Marketing to Operations across any and every industry. Each book includes basic fundamentals, important concepts, and standard and well-known principles as well as practical ways of application of the subject matter.
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
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