Supervised Learning

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Supervised Learning

Supervised Learning is a type of machine learning where algorithms are trained using labeled data. This means that the model learns from a dataset that includes both the input features and the corresponding correct outputs. The goal is to enable the model to make predictions or classify new, unseen data based on the patterns it has learned during training.

In supervised learning, there are two main types of problems: classification and regression. Classification involves predicting discrete labels, such as identifying whether an email is “Spam” or “Not Spam.” Regression, on the other hand, deals with predicting continuous values, such as estimating the price of a car based on its features ²³.

Supervised learning is widely used in various applications, including image recognition, spam detection, and medical diagnosis. By feeding the model with a large amount of labeled data, it can learn to generalize and make accurate predictions on new data ¹³.

If you have more questions about supervised learning or related topics, feel free to ask!

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