
Introduction to Machine Learning
Bechoo Lal(Author)
Delve Publishing
Will be published approx. on 30. June 2026
Book
Paperback/Softback
207 pages
978-1-77956-300-2 (ISBN)
Description
This book is a comprehensive introduction to the principles and techniques of machine learning. It covers key topics such as supervised and unsupervised learning, neural networks, decision trees, support vector machines, and clustering algorithms. The content is structured to guide readers from basic concepts to more advanced topics, ensuring a thorough understanding of both theoretical foundations and practical applications. Real-world examples, case studies, and hands-on exercises are included to illustrate the application of machine learning techniques in various domains such as finance, healthcare, and technology.
More details
Language
English
Place of publication
Canada
Publishing group
Arcler Press
Target group
College/higher education
Product notice
Library binding
Dimensions
Height: 254 mm
Width: 203 mm
ISBN-13
978-1-77956-300-2 (9781779563002)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Bechoo Lal, PhD. became a Member (M) of IAENG: International Association of Engineers, USA with membership (108820) in 2010, a Senior Member (SM) in 2019. A doctorate PhD in Computer Science, PhD- Information System from University of Mumbai, Master from Banaras Hindu University (BHU), PGP- Data Science from Purdue University, USA. Currently working as a Associate Professor in Department of Computer Science & Engineering, KLEF- KL University Vijayawada Campus Andhra Pradesh, India. His research areas are data science, big data analytics and Machine Learning.
Content
1 Introduction to Machine Learning
2 Unsupervised Learning
3 Supervised Learning: Linear Regression
4 Decision Trees
5 Artificial Neural Networks
6 Reinforcement Learning
7 Applications of Machine Learning in Industrial Sectors
8 Issues of Machine Learning for Society
2 Unsupervised Learning
3 Supervised Learning: Linear Regression
4 Decision Trees
5 Artificial Neural Networks
6 Reinforcement Learning
7 Applications of Machine Learning in Industrial Sectors
8 Issues of Machine Learning for Society