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Supervised learning is a type of machine learning where the model is trained on labelled data — input-output pairs where the correct answer is provided. The model learns to map inputs to outputs and can then predict the correct output for new, unseen inputs.
Supervised learning is the most widely used ML approach in industry, powering applications from email spam detection to medical diagnosis.
A hospital trains a supervised learning model on thousands of X-ray images labelled by radiologists as 'normal' or 'abnormal' to help screen patients automatically.
Unsupervised Learning
Unsupervised learning is a type of machine learning where the model is trained on data without labelled answers. The model discovers hidden patterns, groupings, and structures in the data on its own, without being told what to look for.
Classification
Classification is a type of supervised learning where the model learns to assign input data to predefined categories or labels. The model is trained on labelled examples and then predicts which category new, unseen data belongs to.
Regression
Regression is a type of supervised learning where the model predicts a continuous numerical value rather than a category. It learns the relationship between input features and a continuous output variable from labelled training data.
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