Machine learning is simply computers learning from and making predictions on data using algorithms. The outcome of this learning is a model that is a function or collection of functions explaining the patterns in data that enables the computer to make predictions or take actions. This implies that humans enable computers to learn without explicitly programming them [1]. We need machine learning algorithms to build models that represents data and explain the variations and patterns in it. Hence, algorithms are central to machine learning in extracting knowledge and insight from data.
The term machine learning was coined in 1959 by Arthur Samuel, an American pioneer in the field of artificial intelligence. His Checkers-playing program is considered the first example of a machine, learning an algorithm without being explicitly programmed, but by seeing examples of previous checkers moves.