Machine Learning Essentials You Always Wanted to Know

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

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.

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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

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