INTRODUCTION
As an empirical and specialized field of data science and a dominant sub-field of AI, machine learning1 describes the ability of computer models to learn from data and perform cognitive reasoning without direct programming.2 This is a process known as self-learning—an exciting but somewhat vague concept that underpins machine learning. While the human programmer maintains ownership of variable selection and setting algorithm learning hyperparameters (settings), the decision model interprets patterns and generates an output without a direct command. This course of action serves as a major distinction from traditional computer programming where computers are designed to produce fixed outputs in response to pre-programmed commands.
The initial blueprints for machine learning were conceived by Arthur Samuel while working for IBM as an engineer in the late 1950s. Samuel defined machine learning as a subfield of computer science that provides computers...
