Machine learning is a subfield of Artificial Intelligence (AI) that allows computers to learn to program themselves through experience. It is defined as the science of developing algorithms and statistical models that computer systems use to perform tasks relying on patterns and inference instead of explicit programming. Machine learning algorithms process large quantities of historical data and identify data patterns, allowing them to predict outcomes more accurately from a given input data set.
There are three main types of machine learning: supervised, unsupervised, and reinforcement learning. Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. Unsupervised machine learning, on the other hand, looks for patterns in unlabeled data. Reinforcement learning trains machines through trial and error to take the best action by establishing a reward system.
The central idea behind machine learning is an existing mathematical relationship between any input and output data. The machine learning model does not know this relationship in advance, but it can guess if given sufficient data sets. This means every machine learning algorithm is built around a modifiable math function, allowing computer systems to mathematically link complex data points and produce accurate outputs.