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Open it Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence . Machine learning algorithms build a mathematical model based on sample data, known as "training data ", in order to make predictionsOriginal toplevel document
Machine Learningtection [show] Artificial neural networks [show] Reinforcement learning [show] Theory[show] Machine-learning venues[show] Glossary of artificial intelligence [show] Related articles[show] v t e <span>Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence . Machine learning algorithms build a mathematical model based on sample data, known as "training data ", in order to make predictions or decisions without being explicitly programmed to perform the task.[1] [2] :2 Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision , where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task. Machine learning is closely related to computational statistics , which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning .[3] [4] In its application across business problems, machine learning is also referred to as predictive analytics . Contents 1Overview 1.1Machine learning tasks 2History and relationships to other fields 2.1Relation to data mining 2.2Relation to optimization 2.3Relation to statistics 3Theory 4ApproacSummary
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