Machine learning is the scientific process of training systems to act upon data without requiring explicit, programmed instructions. A subtype of Artificial Intelligence (AI), machine learning leverages algorithms and statistical models to identify patterns and predict future outcomes.
Most machine learning initiatives fit within two models: supervised and unsupervised. Supervised machine learning begins with a known, labeled dataset — often called “training data” — and uses that data to make predictions, which are compared against actual outcomes in order to further refine the algorithm. Unsupervised data leverages unlabeled data in order to provide a deeper understanding of how computers identify patterns.
Machine learning can be applied to a number of business applications, and it is particularly useful in areas where conventional algorithms have proven insufficient. Examples of machine learning innovation include self-driving cars, email filtering applications and speech-recognition software.
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