Applied Machine Learning - From Cyber Security to Medical Diagnosis (Paperback)

Zam Ab

ISBN 13: 9798253936283

Published by Independently Published, 2026

About this Item

Paperback. This book is designed as a practical and application-oriented introduction to machine learning, with a strong focus on real-world domains such as financial fraud detection, medical diagnosis, and customer analytics. Rather than presenting machine learning only through theory, the book emphasizes hands-on learning, real datasets, and practical scenarios that allow readers to understand how machine learning techniques are implemented in real environments.The chapters are structured in a progressive manner so that readers can move from foundational concepts to more advanced applications and practical implementations. Each chapter combines conceptual explanations, diagrams, case studies, and Python-based exercises so that readers can develop both theoretical understanding and practical skills.The book currently consists of nine chapters, each designed to address a specific aspect of machine learning and its applications. 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 # 9798253936283

Bibliographic Details

• Title: Applied Machine Learning - From Cyber Security to Medical Diagnosis (Paperback)
• Publisher: Independently Published
• Publication Date: 2026
• Language: English
• ISBN 13: 9798253936283
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book is designed as a practical and application-oriented introduction to machine learning, with a strong focus on real-world domains such as financial fraud detection, medical diagnosis, and customer analytics. Rather than presenting machine learning only through theory, the book emphasizes hands-on learning, real datasets, and practical scenarios that allow readers to understand how machine learning techniques are implemented in real environments.
The chapters are structured in a progressive manner so that readers can move from foundational concepts to more advanced applications and practical implementations. Each chapter combines conceptual explanations, diagrams, case studies, and Python-based exercises so that readers can develop both theoretical understanding and practical skills.
The book currently consists of nine chapters, each designed to address a specific aspect of machine learning and its applications.