Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Name: Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications
Brand: Wiley-Scrivener
SKU: 9781119821250
Price: 14439.0 INR
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by Pardeep Singh

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ISBN13: 9781119821250
Binding: Hardcover
Subject: N/A
Publisher: Wiley-Scrivener
Publisher Imprint: Wiley-Scrivener
Publication Date: March 2, 2022
Pages: 400
Original Price: USD 242.95
Language: English
Edition: N/A
Item Weight: 420 grams
BISAC Subject(s): Artificial Intelligence / General

About The Book

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING

The 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.

About The Author

Pradeep Singh PhD, is an assistant professor in the Department of Computer Science Engineering, National Institute of Technology, Raipur, India. His current research interests include machine learning, deep learning, evolutionary computing, empirical studies on software quality, and software fault prediction models. He has more than 15 years of teaching experience with many publications in reputed international journals, conferences, and book chapters.

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