Machine learning's core objective is to get a system to improve at a task through exposure to data, rather than through rules written by hand. Computer scientist Tom Mitchell formalized this in 1997: a program learns if its performance on a task, measured by a defined metric, improves as it processes more experience. That experience is typically a dataset, and improvement is measured against a benchmark, not intuition.
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