This book provides a hands-on introduction to Machine Learning (ML) from
a multidisciplinary perspective that does not require a background in
data science or computer science. It explains ML using simple language
and a straightforward approach guided by real-world examples in areas
such as health informatics, information technology, and business
analytics. The book will help readers understand the various key
algorithms, major software tools, and their applications. Moreover,
through examples from the healthcare and business analytics fields, it
demonstrates how and when ML can help them make better decisions in
their disciplines.
The book is chiefly intended for undergraduate and graduate students who
are taking an introductory course in machine learning. It will also
benefit data analysts and anyone interested in learning ML approaches.