Via Wikipedia:

Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, where the latter sub-field focuses more on exploratory data analysis and is known as unsupervised learning.[

Machine Learning is a field in computer science focusing on teaching machines to see patterns in data. It is often used to build predictive models for classification, clustering, ranking, or  recommender system sin e-commerce. In supervised machine learning there is generally some dataset, a feature matrix X of observations with knownt ruth labels y that an algorithm should be trained to predict.
The simplest case of classification is binary classification. For example, based on some weather data for a location, will it rain tomorrow? Based on previously known data X, with a boolean label  vector y (yi∈{0, 1}), a classification algorithm can be taught to see common patterns that lead to rain. It is also possible to use the same algorithms to predict the risk of rain as a probability between 0 and 100%.

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