Machine learning has become a cornerstone of modern technology, driving innovations across industries such as healthcare, finance, energy, and automation. As organizations increasingly rely on Data-driven decision-making, a strong understanding of machine learning fundamentals is essential for both students and professionals to remain relevant and effective in their roles.

This book provides a structured and intuitive learning experience through a Q&A format. It begins with foundational machine learning concepts, followed by detailed coverage of supervised and unsupervised learning techniques. The book explores key algorithms such as logistic regression, support vector machines (SVM), decision trees, and ensemble methods. It then progresses to advanced topics, including neural networks, dimensionality reduction, and modern trends like large language models (LLMs). Dedicated chapters on practical implementation using Python libraries, real-world applications, and interview-focused questions ensure a well-rounded understanding of both theory and practice.

By the end of this book, readers will have developed strong conceptual clarity and practical insight into machine learning techniques. They will be equipped to apply these concepts in real-world scenarios, approach problems with confidence, and perform effectively in academic, research, or industry roles.

What you will learn

● Learn supervised and unsupervised learning techniques with clarity.

● Apply dimensionality reduction techniques for efficient data analysis.

● Gain insights into neural networks and deep learning fundamentals.

● Work with Python libraries for practical machine learning implementation.

● Prepare effectively for interviews with structured Q&A practice.

Who this book is for

This book is primarily intended for undergraduate students pursuing B.Tech, MCA, and related programs in computer science, artificial intelligence, and data science, as well as postgraduate students aiming to build strong fundamentals. It is also suitable for aspiring data scientists, machine learning engineers, and software professionals seeking conceptual clarity and practical understanding.

Table of Contents

1. Basics of Machine Learning

2. Foundation of Supervised Machine Learning

3. Advanced Supervised Machine Learning

4. Ensemble Learning Techniques

5. Foundations of Unsupervised Learning

6. Unsupervised Machine Learning Algorithms

7. Dimensionality Reduction

8. Neural Networks

9. Sequential Models

10. Working with Python ML Libraries

11. Recent Trends in Machine Learning

12. Interview Questions and Concept Checks

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About the Author

Rohan Banerjee is a seasoned data science professional with over 15 years of experience in advanced analytics and ML. He began his professional journey in 2011 after earning his M.Tech in electronics and electrical communication engineering from the Indian Institute of Technology, Kharagpur. Over the years, he has worked with reputed organizations such as TCS Research and Baker Hughes, where he has contributed to the development of scalable, data-driven, and AI-powered solutions across diverse industry domains. Rohan has a strong academic and research orientation, with more than 50 technical publications in leading Tier 1 journals and international conferences. His work spans core machine learning, deep learning, and applied AI, reflecting a balanced blend of theoretical depth and practical expertise. His contributions demonstrate a consistent focus on solving real-world problems using cutting-edge AI techniques.

Uddalok Sen is an experienced academician with over 18 years of teaching and research experience in the field of artificial intelligence. He currently serves as an assistant professor in the Department of Information Technology at MCKV Institute of Engineering, Liluah, Howrah. Over the course of his academic career, he has served as an assistant professor at multiple engineering institutions, playing a key role in shaping the learning journey of students in AI and related domains. His teaching philosophy emphasizes conceptual clarity, practical relevance, and continuous exploration. His research interests include machine learning, deep learning, and federated learning, with a focus on advancing both theoretical understanding and applied research. Uddalok completed his B.Tech from Meghnad Saha Institute of Technology, Kolkata, and his M.Tech from the National Institute of Technology, Durgapur. He remains deeply committed to teaching, mentoring, and inspiring the next generation of engineers and researchers in artificial intelligence and machine learning.

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Mastering Machine Learning Through Questions and Answers

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