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Artificial Intelligence Essentials, Machine Learning Essentials & Data Analytics Essentials – Learn AI, ML, and Data Analytics with Hands-On Practice, Real-World Use Cases— Set of 3 Books
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Artificial Intelligence Essentials, Machine Learning Essentials & Data Analytics Essentials – Learn AI, ML, and Data Analytics with Hands-On Practice, Real-World Use Cases— Set of 3 Books
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Step into the world of AI and data with this beginner-friendly 3-book set built for practical learning. From understanding artificial intelligence and machine learning to building strong data analytics foundations, you’ll gain clear explanations and real-world applications that connect concepts to how they’re used in today’s industries.
What’s Inside:
- Artificial Intelligence Essentials You Always Wanted to Know:
- Understand what AI is, how it evolved, and where it’s headed—without heavy jargon.
- Explore practical use cases in healthcare, finance, retail, education, and business operations.
- Get simplified, beginner-friendly fundamentals of NLP, computer vision, and deep learning.
- Learn the essentials of bias, transparency, explainability, and AI governance.
- Machine Learning Essentials You Always Wanted to Know:
- Understand the fundamentals of machine learning (supervised, unsupervised, and reinforcement learning).
- Implement essential ML techniques, including regression, classification, clustering, and evaluation metrics.
- Explore core algorithms such as decision trees, KNN, SVM, and ensemble methods (bagging, boosting).
- Prepare for real-world ML projects by understanding how to design and develop ML applications end-to-end.
- Data Analytics Essentials You Always Wanted to Know:
- Learn the fundamentals of data analytics, including key tools, techniques, and methodologies.
- Explore big data analytics and its challenges, ethical issues, and privacy concerns.
- Strengthen your data management skills with clear guidance on sources, types, preprocessing, storage, and retrieval.
- Apply your learning with real-world case studies and self-assessment tests.
This set offers a complete roadmap to mastering AI, machine learning, and data analytics—perfect for professionals, career switchers, and curious learners looking to enhance their skills in these fast-evolving fields.
Pages: 756 pages
Paperback (ISBN): 9781636517216
Category: Business & Economics
Author: Karthik Chandrakant, Dhairya Parikh, Dr. Bianca Szasz, Vibrant Publishers
Click on individual book titles below to view their complete Table of Contents.
Machine Learning Essentials You Always Wanted to Know
Data Analytics 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.
Dr. Bianca Szasz is a Ph.D. holder in Space Engineering. In over 14 years of experience in engineering and a dedicated focus of 4 years in data analytics, she has used data analytics in a variety of innovative projects, like post-processing of the wind tunnel test results and the analysis of high enthalpy heating test results. Her enthusiasm for data analytics eventually expanded beyond using it for work. Now she is passionate about educating the future generation of data analysts.
Karthik Chandrakantis 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.
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.
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
It is an entry-level teaching textbook, easy to understand, suitable for the preliminary classes in a college course, or serves as a prerequisite reading for a data course.
-- Ting Zhang, Ph.D. Harry Y. Wright Chair, Associate Professor at Merrick School of Business
I think this text is a comprehensive yet succinct manual. It gives a usable introduction to data analytics and is easy to read and comprehend. An added benefit is that the book contains real-world case studies and interesting facts to help enhance existing knowledge.
A key strength of this text is the option to choose the topics that you want to focus on and the resources that you want to use. This flexibility makes self-directed learning a great option for busy professionals who want to add data analytics skills to their repertoire. If you're interested in self-directed learning for data analytics, there are a few things you need to do to get started, and this text will help!
-- David E Reva Instructor - CIS, Kalamazoo Valley Community College
The book is written in a way that makes it easier for complete beginners like me to start to understand the world of data analytics. Every chapter has a helpful summary and a quiz to test your knowledge. Fascinating!
-- Chiara Colombo, Sr Contracts Analyst, Yahoo! Inc.
Absolutely valuable book that can help to structure your knowledge in data science and data analytics. If you are at advanced level, the book may be not for you however if you are looking to shape your experience or interested in switching career to date rekated, this book is a great help. It can provide an overview of the field, present a structured knowledge set and give a good understanding.
-- Darya Yegorina, DBA - Chief of Staff Strategy & Sales Ops, Verizon
Thanks for the opportunity to review this book. A really good introduction to data analytics, set out logically and sequentially building knowledge and understanding as I read. I was able to put into practice what I was learning and really appreciated the summary and quizzes.
Recommend.
-- Emma Dresser, Senior Project Manager
This book is a comprehensive and detailed "map" of Data Analytics subject, which a professional can use in its first years of career. The book covers everything you need to know at the beginning of the journey and will help you make wise decisions such as which software tool to use to solve a specific problem and what are its advantages and disadvantages. The book abounds with practical examples, quizzes and good practice tips.
-- Cătălin Neacșu, Doctor of Philosophy (Ph.D) in Physics
The book is an excellent resource for anyone interested in data analytics. Whether you're a student, professional, or simply a curious learner, this book provides the knowledge and tools needed to navigate and excel in the world of data analytics.
-- Tamer Abu Rouk, Space Engineer
Great book! It helped me understand all we can do with data analytics tools and, thanks to this book, I have started to implement some data analytics techniques at my own job!
-- Sara Szasz, Chief Economist
This book is excellent as it provides a versatile guide to data analytics for all levels, catering both to beginners as well as for those more experienced in the field. Beginners will appreciate how it simplifies complex concepts, offering clear, structured chapters that make learning the basics both manageable and engaging. For seasoned analysts, the book is a valuable reference, delving into advanced topics with clarity and offering practical, real-world applications of various analytics tools.
-- Dana Neacșu, Competitive Analyst
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I coordinate a digital skills development center at a telecommunications company in Istanbul and have been designing AI and data literacy programs for our engineers and managers. This trilogy became the primary reading resource for our most recent twelve-week cohort. Karthik Chandrakant's AI Essentials was particularly effective with our non-technical management participants — his TEDx speaking background and Amazon AI leadership experience shape a writing style that makes complex concepts like transformer architectures and generative AI models feel genuinely understandable without sacrificing accuracy. Dhairya Parikh's ML Essentials gave our technical engineers the hands-on Python coding depth they needed across classification, clustering, ensemble methods, and neural networks. Dr. Szasz's Data Analytics Essentials anchored both groups with shared data methodology vocabulary. The chapter quizzes and glossaries in all three volumes made self-paced revision between sessions practical and effective. A well-matched and professionally authored trilogy.
I lead the data science and AI function at an IT services company in Mysuru and have been building a structured upskilling program for our engineers and analysts over the past year. This trilogy became the required reading for our sixteen-week program across two consecutive cohorts. Dr. Szasz's Data Analytics Essentials was the most universally praised volume — her coverage of data sources, preprocessing, storage, retrieval, big data challenges, and ethical and legal frameworks addressed foundational gaps that our team had accumulated over years of ad hoc learning. Dhairya Parikh's Machine Learning Essentials gave our technical participants the algorithmic depth they required — from supervised and unsupervised learning through ensemble methods, deep learning, and an LLM introduction that was refreshingly current. Karthik Chandrakant's AI Essentials provided the strategic and conceptual AI layer that helped our business analysts connect ML outputs to real organizational applications. Cohort feedback across both programs was the most positive we have received for any upskilling initiative.
I am a General Manager of Digital Strategy at a manufacturing conglomerate in Cincinnati and have been steering our company's AI and data adoption journey for the past two years without formal training in any of the three disciplines. This three-book set resolved that gap more effectively than any other resource I tried. Dr. Bianca Szasz's Data Analytics Essentials — informed by her Space Engineering Ph.D. and 14-plus years of engineering and analytics experience — gave me the data methodology and ethical framework foundation that our transformation work assumed I already had. Dhairya Parikh's Machine Learning Essentials built on that with supervised, unsupervised, and deep learning concepts through hands-on Python coding that made algorithms feel operational rather than theoretical. Karthik Chandrakant's AI Essentials — shaped by 13-plus years at Amazon and Mu Sigma — provided the strategic AI layer covering NLP, computer vision, generative AI, and responsible governance that I needed most urgently for leadership conversations. An indispensable organizational resource across all three volumes.
I run an AI and data strategy consulting practice in San Francisco that advises mid-market companies on building internal AI capabilities. Recommending a single three-book set that covers data analytics methodology, machine learning implementation, and AI strategy in a logically sequenced, consistently authored way has been something my clients have asked for repeatedly. This trilogy is now my definitive answer to that request. Dr. Bianca Szasz's Data Analytics Essentials is methodically organized and covers data analysis tools, big data challenges, and ethical frameworks with the rigor her engineering background enables. Dhairya Parikh's Machine Learning Essentials bridges theory and hands-on Python coding more effectively than most beginner ML texts — the ensemble methods and LLM introduction chapters are particularly current and practically valuable. Karthik Chandrakant's AI Essentials is the volume I share most frequently with C-suite clients — his Amazon and Mu Sigma background produces a text that speaks to AI strategy, responsible governance, and business application with rare authority. An outstanding and well-sequenced trilogy at an exceptional price point.
I was a quality assurance engineer at a software company in Noida who decided to transition into an AI engineering role after watching our data science team's impact grow. I needed structured, self-paced resources that covered data analytics methodology, ML implementation, and AI strategy — in that order — without assuming prior data science knowledge. This trilogy was the most logically sequenced set I found. Dr. Szasz's Data Analytics Essentials gave me the data methodology and analytical thinking foundation I had been missing — the chapters on data preprocessing, big data tools, and ethical frameworks were genuinely new knowledge for someone from a QA background. Dhairya Parikh's ML Essentials introduced me to algorithm families, hands-on Python coding across real datasets, and a current LLM introduction that I found immediately applicable to the work our AI team was doing. Karthik Chandrakant's AI Essentials gave me the strategic and conceptual vocabulary to participate credibly in AI architecture discussions. I was offered an AI engineering role six months into working through this set.
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