Mastering Machine Learning Through Questions and Answers : Essential concepts, theory, models, and practical examples (English Edition)
Language: English
Published by BPB Publications, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 73.90
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - 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 FORThis 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.…
Seller Inventory # 9789378544545
- Title
- Mastering Machine Learning Through Questions and Answers : Essential concepts, theory, models, and practical examples (English Edition)
- Author
- Rohan Banerjee
- Publisher
- BPB Publications
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9378544541
- ISBN 13
- 9789378544545
- Item weight
- 665 grams
- Dimensions
- 235x191x19 mm
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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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