Machine learning is an area of artificial intelligence (AI) and computer science that focuses on using data and algorithms to emulate the way humans learn, with the goal of steadily improving accuracy.
Machine learning has a long history at IBM. With his research (PDF, 481 KB) (link sits outside IBM) on the game of checkers, one of its own, Arthur Samuel, is credited with coining the term “machine learning.” In 1962, self-proclaimed checkers master Robert Nealey played the game on an IBM 7094 computer and lost to it. This achievement may appear insignificant in comparison to what is possible now, yet it is regarded as a crucial milestone in the field of artificial intelligence. The technological advancements around us will continue to accelerate over the next few decades.
Some of the creative technologies we know and appreciate today, such as Netflix’s recommendation engine or self-driving cars, will be made possible by increased storage and processing capability.
Machine learning is a crucial part of the rapidly expanding discipline of data science. Algorithms are trained to generate classifications or predictions using statistical approaches, revealing crucial insights in data mining initiatives. Following that, these insights drive decision-making within applications and enterprises, with the goal of influencing important growth KPIs. As big data becomes more prevalent, the demand for data scientists will rise, necessitating their assistance in identifying the most important business issues and, as a result, the data needed to answer them.
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