A Comprehensive Analysis of Classical Machine Learning and Modern Deep Learning Methodologies

Abstract

Over the past decade, artificial intelligence (AI) has become a popular subject both within and outside of the scientific community; an abundance of articles in technology and non-technology-based journals have covered the topics of Machine Learning, Deep Learning, and Artificial Intelligence. Artificial Intelligence has started to become the mainstay of a number of applications online and in the market worldwide. While AI takes a front seat, Classical Machine Learning algorithms have been around for nearly five decades and continue to be the bedrock of future development and research in the field of machine learning. Besides this, deep learning is the current and a stimulating field of machine learning. Yet there still remains confusion around AI, ML, and DL. Despite their strong associations, the names cannot be used interchangeably. In order to better communicate these concepts to a clinical audience, we (try to) avoid technical jargon in our review study. The purpose of the paper is to familiarize the reader with the various machine learning and deep learning approaches as well as the various kinds of algorithms that are the foundation of the machine learning field

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