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.
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