1st Edition
by Pradeep Singh (Editor)
FUNDAMENTALS AND METHODS OF MACHINE AND
DEEP LEARNINGThe book provides a practical approach by explaining the
concepts of machine learning and deep learning algorithms, evaluation of
methodology advances, and algorithm demonstrations with applications.
Over the past two decades, the field of
machine learning and its subfield deep learning have played a main role
in software applications development. Also, in recent research studies,
they are regarded as one of the disruptive technologies that will
transform our future life, business, and the global economy. The recent
explosion of digital data in a wide variety of domains, including
science, engineering, Internet of Things, biomedical, healthcare, and
many business sectors, has declared the era of big data, which cannot be
analysed by classical statistics but by the more modern, robust machine
learning and deep learning techniques. Since machine learning learns
from data rather than by programming hard-coded decision rules, an
attempt is being made to use machine learning to make computers that are
able to solve problems like human experts in the field.
The goal of this book is to present
a??practical approach by explaining the concepts of machine learning and
deep learning algorithms with applications. Supervised machine learning
algorithms, ensemble machine learning algorithms, feature selection,
deep learning techniques, and their applications are discussed. Also
included in the eighteen chapters is unique information which provides a
clear understanding of concepts by using algorithms and case studies
illustrated with applications of machine learning and deep learning in
different domains, including disease prediction, software defect
prediction, online television analysis, medical image processing, etc.
Each of the chapters briefly described below provides both a chosen
approach and its implementation.
Audience
Researchers and engineers in artificial intelligence, computer scientists as well as software developers.
