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Artificial Intelligence Essentials, Machine Learning Essentials, Graph Machine Learning Essentials & Python Essentials — Set of 4 Books
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Artificial Intelligence Essentials, Machine Learning Essentials, Graph Machine Learning Essentials & Python Essentials — Set of 4 Books
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Build a strong foundation in Artificial Intelligence, Machine Learning, Graph Machine Learning, and Python programming with this comprehensive 4-book collection. Designed for students, aspiring AI professionals, developers, and career switchers, this set combines practical learning, hands-on exercises, and real-world applications to help you develop in-demand technical skills.
Book 1 – Graph Machine Learning Essentials
- Grasp the essentials of graphs, node embeddings, and core learning tasks
- Master Graph Neural Networks through message passing and hands-on implementation in PyTorch Geometric
- Apply graph techniques to real-world problems: fraud detection, cybersecurity, drug discovery, and personalized recommendations
Book 2 – Artificial Intelligence Essentials You Always Wanted to Know
- Build a solid understanding of AI fundamentals, machine learning, deep learning, NLP, computer vision, and Generative AI
- Learn supervised, unsupervised, and reinforcement learning concepts
- Discover ethical AI practices, real-world industry applications, and access AI career preparation resources
Book 3 – Machine Learning Essentials You Always Wanted to Know
- Master core machine learning concepts with hands-on coding
- Learn supervised, unsupervised, reinforcement, and deep learning algorithms
- Develop machine learning models through real-world applications
Book 4 – Python Essentials You Always Wanted to Know
- Learn Python programming from the ground up with beginner-friendly explanations
- Practice data structures, object-oriented programming, data analytics, and modular programming
- Apply Python to analyze data and uncover valuable business insights
Whether you're beginning your AI journey or expanding your technical expertise, this comprehensive set provides the knowledge and practical skills needed for modern AI, machine learning, graph data, and Python development.
Pages: 1042 pages
Paperback (ISBN): 9781636517810
Category: Business & Economics
Author: Karthik Chandrakant, Dhairya Parikh, , Shawn Peters, Karthik Chandrakant, Vibrant Publishers
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.
Karthik Chandrakantis a TEDx speaker and AI leader with 13+ years at Amazon and Mu Sigma, known for demystifying AI and mentoring future innovators.
Shawn Petershas 19 years of teaching experience, is certified in Python Programming Teaching from the College of the North Atlantic, and also specializes in JavaScript and Java.
Pintu Kumaris 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
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
This book really is excellent. I hope this review reflects that:
Python Essentials You Always Wanted to Know by Shawn Peters provides a brilliant approach to learning Python. It not only moves you through all aspects of programming in Python, but with a focus on using the language to address and efficiently solve problems of many sorts.
Its approach is novel in that it encourages the reader to play a lead role in breaking down and thinking through problems and provides expert guidance on how to do this. It also includes quizzes and answers so that you can test yourself on what you’ve learned.
You can read the book from front to back or jump into whatever aspect of programming is challenging you at the moment. Whether you’re a beginning programmer or an experienced programmer looking to advance your coding skills or approach to problem-solving in Python, you’re going to appreciate this book.
-- Sandra Henry-Stocker, NetworkWorldThis book offers a beginner-friendly approach to learning Python, focusing not only on syntax but also on problem-solving, which is key for effective programming. The author’s clear intent is to make coding enjoyable and relevant, providing examples that are simple yet significant to help readers follow along. The emphasis is on applying coding skills practically, with quizzes and case studies included to solidify understanding. By the end, readers will grasp essential concepts such as Python syntax, data structures, error handling, and object-oriented programming, all while gaining the confidence to solve real-world problems. Having an understanding of these concepts, you will be well-prepared to tackle more advanced Python topics and apply your skills to real-life coding challenges.
-- LooYee NG, Solutions Architect at CTMGWhat a great introduction to this topic, especially for a non-programmer like myself! The examples provided are easy to understand and apply, allowing me to try it out for myself to further cement my understanding of the steps involved. Read, See, Do. A great way to learn. I really enjoyed the quizzes at the end of each chapter, as they emphasized what the important takeaways were and reinforced what I did know - and what I didn’t. I’ll probably never become a programmer, but understanding how programming works is an asset to any user.
-- Sharon Peach B.Sc, B.Ed, M.EdThis beginner-friendly guide makes mastering this versatile language accessible for everyone. It provides clear explanations and practical examples while avoiding technical jargon, making it perfect for absolute beginners and those at various skill levels. Even as someone who isn’t usually into programming, I found it easy to follow!
-- Samantha NicholsNet Galley ReviewerI had been looking for a companion guide to go along with some introductory python courses I had been taking and this was perfect, it offered what I needed as a beginner and helped to put steps into writing so I could easily refer back to them when I needed to!
-- Victoria MadiganNet Galley ReviewerI have not yet implemented all the lessons/the coding exercises in this book but I checked them all out.
I have a child who is interested in coding. and I love to learn to know more about coding myself in my spare time. This is a good resource for almost total beginners like us.
-- DidemDurak AkserNet Galley Reviewer
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