Machine Learning (Paperback)

D. Magdalene Delighta Angeline

ISBN 10: 6209928005 / ISBN 13: 9786209928000

Published by LAP Lambert Academic Publishing, 2026

About this Item

Paperback. The primary aim of this book is to provide students, educators, researchers, and aspiring professionals with a strong foundation in machine learning concepts, algorithms, and practical implementation techniques. It is designed to help learners understand how intelligent systems learn from data, recognise patterns, make predictions, and support decision-making in real-world applications. This book aims to: - Introduce the fundamentals and types of machine learning.- Explain core supervised, unsupervised, and reinforcement learning techniques. - Develop analytical thinking for selecting suitable algorithms for different problems. - Provide practical knowledge using Python and popular ML libraries. - Bridge theoretical understanding with hands-on experimentation. - Prepare learners for advanced studies, research, and industry applications in AI and data science. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9786209928000

Bibliographic Details

• Title: Machine Learning (Paperback)
• Publisher: LAP Lambert Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6209928005
• ISBN 13: 9786209928000
• Binding: Paperback
• Condition: new

About this title

Synopsis

The primary aim of this book is to provide students, educators, researchers, and aspiring professionals with a strong foundation in machine learning concepts, algorithms, and practical implementation techniques. It is designed to help learners understand how intelligent systems learn from data, recognise patterns, make predictions, and support decision-making in real-world applications. This book aims to: - Introduce the fundamentals and types of machine learning.- Explain core supervised, unsupervised, and reinforcement learning techniques. - Develop analytical thinking for selecting suitable algorithms for different problems. - Provide practical knowledge using Python and popular ML libraries. - Bridge theoretical understanding with hands-on experimentation. - Prepare learners for advanced studies, research, and industry applications in AI and data science.