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Artificial Intelligence Essentials & Machine Learning Essentials: Master AI Fundamentals, Machine Learning Algorithms, Deep Learning with Real-World Applications— Set of 2 Books
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Artificial Intelligence Essentials & Machine Learning Essentials: Master AI Fundamentals, Machine Learning Algorithms, Deep Learning with Real-World Applications— Set of 2 Books
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Unlock the future of technology with this two-book beginner-friendly set on Artificial Intelligence and Machine Learning. Whether you’re preparing for a role in AI or strengthening your understanding for work, this combo delivers clear explanations, practical learning, and real-world relevance—without overwhelming jargon.
What's Inside:
Artificial Intelligence Essentials You Always Wanted to Know
- Understand AI fundamentals and explore the latest trends in AI, Machine Learning, Deep Learning, and Generative AI.
- Discover how AI models are built, trained, and deployed, with practical context and real-world relevance.
- Deep dive into Natural Language Processing (NLP), Computer Vision (CV), and Generative AI (including prompt engineering and LLMs) to see how AI solves real problems.
- Learn responsible AI practices, including bias, transparency, explainability, and governance.
Machine Learning Essentials You Always Wanted to Know
- Get started with machine learning basics, its history, everyday use cases, andthe building blocks of an ML system.
- Learn key ML techniques such as regression, classification, clustering, and how to choose the right approach.
- Go beyond the basics with a beginner-friendly introduction to neural networks, CNNs, and RNNs.
- Explore real use cases, how to develop an ML application end-to-end, ethics in ML, and what’s next for the field.
Perfect for business professionals, career switchers, and tech enthusiasts, this set makes AI and ML approachable, actionable, and easy to apply at work or in projects.
Pages: 534 pages
Paperback (ISBN): 9781636517209
Category: Business & Economics
Author: Karthik Chandrakant, Dhairya Parikh , Vibrant Publishers
Click on individual book titles below to view their complete Table of Contents.
Machine Learning Essentials You Always Wanted to Know
Artificial Intelligence Essentials You Always Wanted to Know
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 Chandrakant is a TEDx speaker and AI leader with 13+ years at Amazon and Mu Sigma, known for demystifying AI and mentoring future innovators.
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.
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.
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
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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I used both books intensively for three months before interviewing for data science roles. ML Essentials covers the algorithmic foundations that come up in technical screens — regression, classification, clustering, neural networks, and model evaluation metrics. AI Essentials gave me strategic context on NLP, computer vision, and generative AI that impressed interviewers during conceptual discussions. I received two offers and credit this combo as my primary study resource.
What sets this 2-book combo apart is how deliberately the content builds. AI Essentials establishes conceptual clarity — AI history, ML foundations, deep learning, NLP, and generative AI — while ML Essentials dives into the algorithmic detail with hands-on Python implementations. Parikh's background as a University of Waterloo data engineer shines in the ML book's treatment of model evaluation, feature engineering, and real-world data pipelines. This is the structured foundation I had been searching for.
Most beginner AI books skip responsible AI and jump straight to hype. Karthik Chandrakant's AI Essentials devotes meaningful space to bias, transparency, explainability, and governance — topics that matter enormously to professionals implementing AI in regulated industries. The ML Essentials mirrors this commitment by addressing ethical considerations in machine learning. As a compliance officer exploring AI governance, this two-book set gave me the technical and ethical grounding I needed.
This two-book set by Karthik Chandrakant and Dhairya Parikh is precisely what I needed to bridge my knowledge gap in AI and machine learning. The AI Essentials covers everything from core fundamentals to generative AI and LLMs, while the ML Essentials provides a step-by-step introduction to supervised and unsupervised learning with real Python examples. Together they form a coherent curriculum that I completed in six weeks alongside a full-time job. Both authors bring genuine practitioner depth to every chapter.
I picked up this set after struggling to follow AI conversations in my organization. Karthik Chandrakant's AI Essentials transformed how I understand the field — his 13-plus years at Amazon and Mu Sigma are evident in every practical example. The ML Essentials by Dhairya Parikh complemented this perfectly, covering regression, classification, clustering, and neural networks in a way that felt approachable rather than intimidating. By the end of both books I was actively contributing to our AI adoption discussions.
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