Computers can learn from data.
Arthur Samuel (1969) defined machine learning as the field of study that gives the computer the ability to learn without being explicitly programmed.
Tom Mitchell (1998): a computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. Machine learning (also known as data mining, pattern recognition and predictive analytics) is used widely in business, industry, science and government, and there is a great shortage of experts in it. The main supervised learning techniques include decision trees, rules, instances, Bayesian techniques, neural networks, model ensembles, and support vector machines. The two main classes of unsupervised learning methods are clustering and dimensionality reduction.
Machine learning algorithms can be distinguished in supervised or unsupervised.
Supervised: we are going to teach computer how to do something - Unsupervised: we are going to let the computer learns by itself.