A STUDY TO COMPARE MACHINE LEARNING AND DEEP LEARNING DIFFERENCES

Abstract

The revolutionary subfields of computer science known as machine learning and deep learning are finding significant application in the business world. The process of teaching computers and other machines how to make predictions based on prior data or actions using examples from their own memory is known as machine learning. Deep learning is a subsection of machine learning that makes use of artificial neural network techniques and algorithms to train and learn from data that is not structured. This allows for learning to take place from data that is not organized. In order to make sense of the mountain of data that is being created each day, there is an urgent need for techniques of data usage and management that are highly automated and technologically advanced. The software for machine learning (ML) and deep learning (DL) is subjected to a thorough investigation that we provide in this work. The study serves as an introduction to the fundamentals of ML and DL. The most widely used approaches and techniques in fields made feasible by technological advancements are investigated next. In conclusion, a business point of view is presented on the two applications of ML and DL that are most often used.