Machine Learning Algorithms and Applications: Theory and Applications - Hardcover

Hardcover

ISBN 10: 1119768853 ISBN 13: 9781119768852

Publisher: Wiley-Scrivener, 2021

Synopsis

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms.

The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

About this title

About the Author

Mettu Srinivas PhD from the Indian Institute of Technology Hyderabad, and is currently an assistant professor in the Department of Computer Science and Engineering, NIT Warangal, India.

G. Sucharitha PhD from KL University, Vijayawada and is currently an assistant professor in the Department of Electronics and Communication Engineering at ICFAI Foundation for Higher Education Hyderabad.

Anjanna Matta PhD from the Indian Institute of Technology Hyderabad and is currently an assistant professor in the Department of Mathematics at ICFAI Foundation for Higher Education Hyderabad.

Prasenjit Chatterjee PhD is an associate professor in the Mechanical Engineering Department at MCKV Institute of Engineering, India.

From the Back Cover

The book is written for experienced and starting machine learning specialists looking to implement solutions to real-world machine learning problems.

Machine Learning Algorithms and Applications shows how one can easily adopt machine learning to build solutions for small applications. It clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, manufacturing, fake profile detection in social media, farming sectors, etc.

For the veteran and new machine learning specialists who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results include comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program

Audience The book is primarily intended for researchers, students, and professionals in computer science, information technology, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners in biomedical fields, manufacturing, supply chain and logistics, agriculture, and Industry 4.0 professionals.

From the Inside Flap

The book is written for experienced and starting machine learning specialists looking to implement solutions to real-world machine learning problems.

Machine Learning Algorithms and Applications shows how one can easily adopt machine learning to build solutions for small applications. It clearly explains the various applications of machine and deep learning for use in the medical field, animal classification, gene selection from microarray gene expression data, sentiment analysis, manufacturing, fake profile detection in social media, farming sectors, etc.

For the veteran and new machine learning specialists who are looking to implement solutions to real-world machine learning problems, this book thoroughly discusses the various applications of machine and deep learning techniques. Each chapter deals with the novel approach of machine learning architecture for a specific application and its results include comparisons with previous algorithms. In order to present a unified treatment of machine learning problems and solutions, many methods based in different fields are discussed, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining. Furthermore, all learning algorithms are explained in a way that makes it easy for students to move from the equations in the book to a computer program

Audience The book is primarily intended for researchers, students, and professionals in computer science, information technology, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners in biomedical fields, manufacturing, supply chain and logistics, agriculture, and Industry 4.0 professionals.

Edition details

• Publisher: Wiley-Scrivener
• Publication date: 2021
• Language: English
• ISBN 10: 1119768853
• ISBN 13: 9781119768852
• Binding: Hardcover
• Edition number: 1
• Number of pages: 368
• Editor: Srinivas Mettu, Sucharitha G., Matta Anjanna