Breakthrough In Applied Machine Learning (2025)
Breakthrough In Applied Machine Learning explores how machine learning is making a real difference beyond academic circles, tackling important challenges across healthcare, logistics, agriculture, and public safety. Rather than getting lost in the complexities of algorithms, this book focuses on how theoretical concepts can be transformed into practical solutions that deliver tangible results. Each chapter combines insights from research and industry, supported by real-world case studies that illustrate how both traditional machine learning methods, such as decision trees and support vector machines and newer approaches, such as deep learning and transfer learning, are used to address pressing issues. Examples include predicting heart disease, forecasting demand, analysing crime patterns, reducing child malnutrition, and boosting agricultural productivity. This approach helps bridge the gap between theory and practice, demonstrating how machine learning can truly improve our world. This book is a great resource for students, researchers, and professionals looking to gain a practical understanding of machine learning. It breaks down the concepts and shows how they can be applied in the real world, making it easier to understand and put into practice.