In traditional computing, input data is fed to a program to generate output. But in machine learning, input data and output data are fed to a machine learning algorithm to generate a function or program that can be used to predict the output of an input according to the learning done on the input/output dataset fed to the machine learning algorithm.
The data available in the wild may be classified into groups, it may form clusters, or it may fit into certain relationships. These are different kinds of machine learning problem. For example, if there is a databank of pre-owned car sale prices with its associated attributes or features, it is possible to predict the price of a car just by knowing the associated attributes or features. Regression algorithms are used to solve these kinds of problem. If there is a databank of spam and non-spam e-mails, then when a new e-mail comes, it is possible to predict whether the new e-mail is spam or non-spam. Classification...