MACHINE LEARNING

Machine learning is a subfield of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how humans learn, gradually improving its accuracy.

Over the last few decades, advances in storage and processing power have enabled some innovative machine learning-based products, such as Netflix’s recommendation engine and self-driving cars.

Machine learning is a critical component of the rapidly expanding field of data science. Algorithms are trained using statistical methods to make classifications or predictions and to uncover key insights in data mining projects. These insights then influence decision making within applications and businesses, ideally influencing key growth metrics. As big data expands and grows, so will the market demand for data scientists. They will be expected to assist in identifying the most pertinent business questions and the data to answer them.

Frameworks are commonly used to create machine learning algorithms.

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UC Berkeley divides a machine learning algorithm’s learning system into three major components.

A Decision Process: Machine learning algorithms are typically used to make a prediction or classification. Your algorithm will generate an estimate about a pattern in the data based on some input data, which can be labeled or unlabeled.

An Error Function: An error function evaluates the model’s prediction. If there are known examples, an error function can compare them to determine the model’s accuracy.

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