How supervised Machine Learning works.

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How supervised Machine Learning works.

A model is prepared through a training process in which it is required to make predictions and it is corrected when those predictions are wrong.

Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

The training process continues until the model achieves a desired level of accuracy on the training data.

Example problems: Classification and Regression.

Example algorithms: Logistic Regression and the Back Propagation Neural Network.

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