Machine Learning – A Survey

Abstract

Machine learning has grown rapidly in the last decade and holds importance not only in the technological industry but even in factories and manufacturing units. It is a notion that has been around for quite long period of time. The notion of automating the application of sophisticated mathematical computations to massive data, on the other hand, has only been around for a few years, but it is getting momentum today. Machine learning is most often utilized by organizations when they implement artificial intelligence systems nowadays, and the phrases machine learning and artificial intelligence are commonly used interchangeably. Machine learning is an artificial intelligence area that allows machines to understand without being explicitly programmed. The growing interest in machine learning is due to several factors such as cheaper computational processing power, high availability of the data, and affordable storage for the data. All of this ensures that models that can evaluate larger, more complicated data and offer faster, quite accurate answers may be created rapidly and automatically, even on a massive scale. An organization's chances of recognizing profitable possibilities or avoiding unforeseen hazards, improve when detailed models are built. The importance can be linked to the accurate predictions without human intervention which can ultimately help take smart actions and make better decisions in the real world. The usefulness of machine learning technology has been acknowledged by most businesses that deal with big volumes of data. Organizations might operate more effectively or gain competitive edge over rivals by harvesting insights from this data frequently in real time. Machine learning is significant because it allows businesses to see insights about customer behavior and company operating patterns while also assisting in the creation of new goods. Every market is evolving, or will evolve, and professionals must comprehend the fundamental concepts, promise, and constraints of machine learning. In this chapter, various types of machine learning and associated techniques have been discussed. The chapter discusses not only supervised but also unsupervised techniques in a comprehensive manner so as to give the reader a solid understanding of the fundamental concepts. Different types of clustering techniques have also been considered along with their advantages and disadvantages. Feature reduction techniques to reduce the dimensionality of the data and various distance measures are extensively covered in the chapter. The chapter provides an insightful understanding to the researchers about numerous applications of machine learning and future scope.