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Machine Learning

This introduction to machine learning provides an overview of its history, key definitions, applications, and current business concerns.

What is Machine Learning?

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

Machine learning is an important factor in the rapidly expanding field of data science. Algorithms are trained using statistical methods to make classifications or predictions, revealing 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, who will be required to assist in the identification of the most pertinent business questions and, ultimately, the data to answer them.

How does Machine Learning work?

A Decision Process: Machine learning algorithms are typically used to make a prediction or classification. Your algorithm will make 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 is used to evaluate the model's forecasting. If there are known examples, an error function can compare them to determine the model's accuracy.

Model Optimization: If the model fits better to the data points in the training set, weights are adjusted to reduce the gap between the known example and the model estimate. The algorithm will repeat this evaluation and enhance the process, updating weights independently until a certain level of accuracy is reached.

Machine Learning Methods

Machine Learning Models are categorized primarily into three as follows:

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