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Machine Learning Essentials You Always Wanted to Know: A Hands-On Beginner's Guide to Mastering AI, Supervised, Unsupervised, and Deep Learning Algorithms
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Machine Learning Essentials You Always Wanted to Know: A Hands-On Beginner's Guide to Mastering AI, Supervised, Unsupervised, and Deep Learning Algorithms
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Description
- Covers key algorithms and techniques
- Ideal for students and professionals
- Hands-on implementation included
Master the fundamentals of ML and take the first step towards a career in AI!
In today’s rapidly evolving world, machine learning (ML) is no longer just for researchers or data scientists. From personalized recommendations on streaming platforms to fraud detection in banking, ML powers many aspects of our daily lives. As industries increasingly adopt AI-driven solutions, learning machine learning has become a valuable skill. Yet, many find the subject overwhelming, often intimidated by its mathematical complexity. That’s where Machine Learning Essentials You Always Wanted to Know (Machine Learning Essentials) comes in. This beginner-friendly guide offers a structured, step-by-step approach to understanding machine learning concepts without unnecessary jargon. Whether you are a student, a professional looking to transition into AI, or simply curious about how machines learn, this book provides a clear and practical roadmap to mastering ML.
Authored by Dhairya Parikh, an experienced data engineer who returned to academia to refine his expertise, this book bridges the gap between theory and real-world application. It simplifies the core concepts of ML, breaking them down into digestible explanations paired with hands-on coding exercises to help you apply what you learn.
What You’ll Learn:
- The fundamentals of machine learning and how it powers modern technology
- The three key types of ML—Supervised, Unsupervised, and Reinforcement Learning
- How to combine algorithms, data, and models to develop AI-driven solutions
- Practical coding techniques to build and implement machine learning models
Part of Vibrant Publishers’ Self-Learning Management Series, this book serves as a valuable guide for building machine learning skills, enhancing your expertise, and advancing your career in AI and data science.
Bibliographic Details
Pages: 270 pages
Paperback (ISBN): 9781636513775
eBook (ISBN): 9781636513782
Hardback (Color): 9781636513799
Trim Size: 5.5” x 8.5”
Category: Business & Economics, Computers
Author: Dhairya Parikh, Vibrant Publishers
Table of Contents
1. Machine Learning: A Gentle Introduction
1.1 What is Machine Learning?
1.2 Machine Learning: A Historical Overview
1.3 Where is Machine Learning used in Daily Life?
1.4 Overview of a Typical ML System
1.5 Conclusion
Glossary
Quiz
2. Mastering the Fundamentals of Machine Learning
2.1 Types of Data in Machine Learning
2.2 Math and Machine Learning
2.3 Introducing Python and Other Essential Tools
2.4 Conclusion
Glossary
Quiz
3. Supervised Learning: Starting with the Basics
3.1 A Refresher on Supervised Learning
3.2 Linear Regression: The Starting Point
3.3 Logistic Regression: The Fundamental Classifier
3.4 Evaluation Metrics in Supervised Learning
3.5 Conclusion
Glossary
Quiz
4. Going Beyond the Basics: Exploring Non-Linear Models
4.1 Decision Trees: Unraveling the Tree Structure
4.2 K-Nearest Neighbors: Finding Friends in Data
4.3 Support Vector Machines: The Magic of Margins
4.4 Conclusion
Glossary
Quiz
5. Ensemble Techniques: Improving Prediction Power
5.1 Bagging: Harnessing the Power of Multiple Models
5.2 Boosting: Learning from Mistakes
5.3 Advanced Ensemble Models: Introduction to Random Forests and LightGBM
5.4 Conclusion
Glossary
Quiz
6. Unsupervised Learning: Finding Patterns in Data
6.1 Clustering Basics: K-Means and Other Clustering Techniques
6.2 Dimensionality Reduction Techniques: PCA and t-SNE
6.3 Association Rules: Market Basket Analysis
6.4 Conclusion
Glossary
Quiz
7. A Gentle Introduction to Neural Networks and Deep Learning
7.1 Neural Networks - Building Blocks of Deep Learning
7.2 Convolutional Neural Networks: A Smarter Approach for Image Data
7.3 Recurrent Neural Networks: Sequences and Predictions
7.4 Conclusion
Glossary
Quiz
8. Machine Learning in Real-World Scenarios
8.1 Exploring Machine Learning Use Cases across Domains
8.2 How to Develop a Machine Learning Application
8.3 Ethics in Machine Learning
8.4 The Future of Machine Learning
Author
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.
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.
Series
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.
Editorial Reviews
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 Baheti
Data Scientist, TD Bank
Machine 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 Appelskog
Productive Planet, Book Trade Professional
Machine Learning Essentials offers 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 Boczan
Reviewer from UK
Parikh’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. Kromrie
Goodreads Reviewer
Machine 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 Johns
Amazon.com Reviewer
Supplemental Resources
I am a senior business analyst at a financial services company in Boston who had been trying to build genuine machine learning fluency for over a year through online courses that never quite stuck. Dhairya Parikh's book was the resource that finally made the difference. His background — combining technical consulting industry experience with a master's degree from the University of Waterloo specializing in ML and AI — gives the book a dual authority that purely academic texts cannot replicate. The progression from ML fundamentals and learning paradigms through supervised learning techniques, unsupervised clustering, ensemble methods including Bagging, Boosting, and LightGBM, and into deep learning and neural networks is perfectly calibrated for a complete beginner. The hands-on Python coding exercises paired with real datasets and the chapter-wise glossary for quick revision made self-directed learning genuinely efficient. The gentle introduction to Large Language Models at the end was an unexpected and valuable addition. A standout ML primer.
I am a commerce graduate from Nagpur who decided to pivot into data science after noticing how deeply ML was reshaping the financial services industry. This book by Dhairya Parikh was the primary resource for my self-study over five months. What impressed me most was how the author — who himself returned to academia to pursue a master's degree at the University of Waterloo after years in technical consulting — understands exactly what a motivated non-technical learner needs. The three ML learning paradigms — supervised, unsupervised, and reinforcement learning — are explained with real-world analogies that made the concepts immediately graspable. The hands-on coding exercises with guided video tutorials for Python installation and access to real datasets for practice gave me the practical skill-building structure I had not found in any online course. The ensemble models chapter on Bagging, Boosting, and LightGBM prepared me for actual ML job discussions. A genuinely career-changing resource.
I am a software developer at an IT company in Bengaluru who was transitioning from backend development into a machine learning engineering role. This book gave me the conceptual foundation and practical coding experience I needed in a compact, self-study format. Dhairya Parikh's treatment of supervised learning — covering linear and logistic regression, decision trees, random forests, KNN, and SVM — with hands-on Python coding exercises made the algorithms feel understandable rather than intimidating. The ensemble methods chapter on Bagging, Boosting, and LightGBM was directly relevant to the interview questions I encountered. The deep learning chapter's introduction to CNNs and RNNs, and the final section on Large Language Models, gave me the vocabulary to discuss modern AI architectures confidently. The online resources — guided Python installation tutorials and real dataset access — made independent practice straightforward. A highly effective and well-authored ML self-study resource.
I am a management consultant at a strategy firm in Seattle who works regularly with clients undergoing AI and data-driven transformation. Understanding machine learning at a meaningful depth — not just surface-level familiarity — has become essential for credible client conversations. As one Amazon reviewer noted about this book, it made them feel like they could actually start using ML in consulting work, especially on data-heavy projects. I had exactly the same experience. Dhairya Parikh's writing bridges the gap between theory and practice in a way that is rare — each algorithm is explained conceptually, then demonstrated through hands-on Python coding, then connected to real-world business applications. The coverage progresses logically from ML fundamentals through supervised and unsupervised learning, ensemble models, deep learning, and finally LLMs — a sequence that mirrors how ML is actually used in organizational contexts. The chapter glossaries and quizzes make it an efficient self-study resource for time-pressed professionals.
At the level of most beginner ML books, ethics is either absent entirely or limited to a single throwaway paragraph. The dedicated section on ethics in machine learning in this book — covering bias, fairness, accountability, and responsible AI development — is genuinely substantive for an introductory text. As someone who works in a regulated industry where AI deployment requires ethical justification, this section was directly relevant to conversations I have at work. The rest of the content is equally strong: the progression from linear regression through decision trees, SVMs, ensemble methods, and neural networks is well-paced and the hands-on Python coding exercises make each algorithm feel concrete rather than abstract. A 4 because I'd have liked more coverage of model interpretability tools like SHAP.
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