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Artificial Intelligence and Data Science Essentials (4-Book Set)
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Artificial Intelligence and Data Science Essentials (4-Book Set)
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Build today’s most in-demand tech skills with this 4-book beginner set covering AI, Machine Learning, Data Analytics, and Python. Whether you want to understand how AI works, build machine learning models, analyze real-world data, or code confidently in Python, this bundle gives you a clear, structured path from fundamentals to practical application.
What's Inside:
- Artificial Intelligence Essentials You Always Wanted to Know
- Build a strong AI foundation with clear explanations of AI concepts, machine learning, and deep learning.
- Explore NLP, computer vision, and generative AI through simplified, real-world examples.
- Apply AI responsibly and confidently with practical learning, ethical AI guidance, and career prep resources.
- Machine Learning Essentials You Always Wanted to Know
- Master the core machine learning concepts: supervised, unsupervised, and reinforcement learning.
- Learn essential algorithms and techniques, including 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
- Turn raw data into insights with essential tools, techniques, and analytics methodologies.
- Learn faster with real-world case studies that connect concepts to practical outcomes.
- Understand big data, privacy, and regulation, including ethical challenges and legal frameworks.
- Python Essentials You Always Wanted to Know
- Start coding in Python from scratch with clear, jargon-free instructions and practical examples.
- Strengthen core programming fundamentals with data structures, functions, modular programming, OOP, and exception handling.
- Practice with real case studies and explore emerging trends like AI/ML, IoT, web scraping, and web frameworks.
Get the skills that power modern innovation—Artificial Intelligence, Machine Learning, Data Analytics, and Python programming—in one complete bundle packed with beginner-friendly explanations, hands-on practice, and real-world relevance.
Pages: 1056 pages
Paperback (ISBN): 9781636517223
Category: Business & Economics
Author: Karthik Chandrakant, Dhairya Parikh, Dr. Bianca Szasz, Shawn Peters, 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
Python 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.
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.
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
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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I manage a corporate learning and development center at a technology conglomerate in Ankara and have been designing a structured AI and data science upskilling track for our analysts and engineers over the past eighteen months. This four-book set replaced our previous patchwork of online course modules as the primary program resource. Shawn Peters's Python Essentials was accessible even for participants with no prior programming experience — the OOP, error handling, and data structure chapters were most frequently praised. Dr. Szasz's Data Analytics Essentials established the shared data methodology vocabulary our participants needed before tackling ML. Dhairya Parikh's ML Essentials provided the algorithmic depth our technical cohort required, with hands-on coding exercises that produced tangible skills rather than theoretical familiarity. Karthik Chandrakant's AI Essentials — informed by his TEDx speaking experience and Amazon AI leadership — gave every participant the strategic and ethical AI perspective increasingly required in our organizational AI governance discussions. Cohort completion and satisfaction scores were the highest our program has recorded.
I am a Director of Product Management at a healthcare AI company in Boston who entered the AI field from a clinical background and spent years filling conceptual and technical gaps through scattered online resources. This four-book set would have compressed that journey by years. Shawn Peters's Python Essentials — built on 19 years of teaching experience and College of the North Atlantic certification — gave me the programming foundation I never had, with clarity that my scattered tutorials never managed. Dr. Bianca Szasz's Data Analytics Essentials connected data methodology, preprocessing, and ethical frameworks to real professional contexts with her Space Engineering Ph.D. authority. Dhairya Parikh's ML Essentials walked me through supervised, unsupervised, and deep learning through hands-on Python coding that made algorithms feel implementable rather than academic. Karthik Chandrakant's AI Essentials — backed by 13-plus years at Amazon and Mu Sigma — provided the NLP, computer vision, generative AI, and responsible governance layer that directly informs our product decisions. An extraordinary four-volume career investment.
I am a Chief Marketing Officer at a consumer goods company in Denver who had been deeply skeptical of AI tools until my board started asking for an AI strategy. I knew I needed to build genuine AI literacy — not just surface familiarity — and quickly. This four-book set was the resource that made that possible within a single, focused quarter of self-study. Shawn Peters's Python Essentials was the first programming resource that ever made me feel genuinely capable as a coder — his 19 years of teaching clarity show in every explanation. Dr. Szasz's Data Analytics Essentials transformed how I think about data collection, preprocessing, and responsible data governance in a marketing context. Dhairya Parikh's ML Essentials demystified recommendation algorithms, classification models, and neural networks through coding exercises rather than abstract descriptions. Karthik Chandrakant's AI Essentials — authored by a former Amazon AI leader and TEDx speaker — gave me the NLP, generative AI, and responsible AI governance vocabulary I needed to lead credible AI strategy conversations with our leadership team and agency partners. A genuinely transformative four-book investment.
I am a professor of computer applications at a management institute in Coimbatore and have been evaluating this four-book set for integration across our postgraduate data science and AI program. Each volume stands independently as a course text — the standard I apply to all bundled resources before academic adoption. Shawn Peters's Python Essentials is the most pedagogy-conscious Python introduction I have reviewed for non-CS postgraduate students — particularly effective for students from commerce and management backgrounds. Dr. Szasz's Data Analytics Essentials covers legal frameworks, privacy considerations, and ethical data usage with a rigor that makes it suitable for postgraduate research methodology modules. Dhairya Parikh's ML Essentials introduces LLMs and deep learning foundations in a way that is appropriately current for today's AI-focused students. Karthik Chandrakant's AI Essentials includes career preparation content and AI interview guidance that our placement-focused students actively reference. A thoughtfully curated and academically strong four-volume set that I have now recommended for formal adoption across three courses.
I am a civil engineering graduate from Jaipur who decided to transition into AI and data science after realizing how deeply these technologies are reshaping infrastructure and urban planning. Finding a four-book set that covered Python programming, data analytics methodology, machine learning implementation, and AI strategy in a single, logically sequenced package — without assuming prior coding or data science knowledge — was the biggest challenge in my transition. This set resolved it definitively. Shawn Peters's Python Essentials was the most patient and well-structured programming introduction I found — his 19 years of teaching experience are reflected in how naturally every concept scaffolds the next. Dr. Szasz's Data Analytics Essentials gave me the data management, preprocessing, and big data context I needed before touching ML. Dhairya Parikh's ML Essentials introduced algorithm families and hands-on Python coding including an LLM primer that felt genuinely current. Karthik Chandrakant's AI Essentials tied everything together with the strategic AI perspective that helped me communicate with decision-makers. An outstanding and well-matched set.
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