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Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study 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. Topics like Data warehousing , Data visualization , Ontology in data mining .
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Scope and Importance :
Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. 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, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.
When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.
The Machine learning market has been achieving strong revenues. The Global Market Statistics shows that it has reached a revenue height of 7.15USD (billion), 8.15 USD (billion) and 4.12 USD (billion) for services, hardware and software, respectively for the year 2013. The global market statistic also shows a revenue forecast for the global big data market from 2011 to 2017. For 2016, the source projects the global big data market is predicted to grow to more than 45 billion U.S. dollars in revenue.
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This page was last updated on 12th Sep, 2015
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