Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
However, we only recently started realizing its potential when technology became capable of gathering massive amounts of data.
By marrying that data to affordable computers with tremendous processing power and inexpensive storage, the age of machine learning was born. Now, within machine learning, there are two further types that define exactly how a machine learns. These classifications are supervised learning and unsupervised learning. Supervised learning is the type of machine learning in which machines are trained using well "labeled" training data, and on the basis of that data, machines predict the output.
The labeled data means some input data is already tagged with the correct answer. In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. It applies the same concept used when a student learns under the supervision of the teacher. Unsupervised learning, on the other hand, uses machine learning algorithms to analyze and cluster unlabelled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.