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Introduction to Machine Learning and Its Implementation Techniques

Introduction to Machine Learning and Its Implementation Techniques
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Author(s): Arul Murugan R. (Sona College of Technology, India)and Sathiyamoorthi V. (Sona College of Technology, India)
Copyright: 2020
Pages: 25
Source title: Handbook of Research on Applications and Implementations of Machine Learning Techniques
Source Author(s)/Editor(s): Sathiyamoorthi Velayutham (Sona College of Technology, India)
DOI: 10.4018/978-1-5225-9902-9.ch018

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Abstract

Machine learning (ML) is one of the exciting sub-fields of artificial intelligence (AI). The term machine learning is generally stated as the ability to learn without being explicitly programmed. In recent years, machine learning has become one of the thrust areas of research across various business verticals. The technical advancements in the field of big data have provided the ability to gain access over large volumes of diversified data at ease. This massive amount of data can be processed at high speeds in a reasonable amount of time with the help of emerging hardware capabilities. Hence the machine learning algorithms have been the most effective at leveraging all of big data to provide near real-time solutions even for the complex business problems. This chapter aims in giving a solid introduction to various widely adopted machine learning techniques and its applications categorized into supervised, unsupervised, and reinforcement and will serve a simplified guide for the aspiring data and machine learning enthusiasts.

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