Mastering Machine Learning with scikit-learn - Second Edition

Learning from experience

Machine learning systems are often described as learning from experience either with or without supervision from humans. In supervised learning problems, a program predicts an output for an input by learning from pairs of labeled inputs and outputs. That is, the program learns from examples of the "right answers". In unsupervised learning, a program does not learn from labeled data. Instead, it attempts to discover patterns in data. For example, assume that you have collected data describing the heights and weights of people. An example of an unsupervised learning problem is dividing the data points into groups. A program might produce groups that correspond to men and women, or children and adults. Now assume that ...