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Supervised vs. Unsupervised Machine Learning

Supervised machine learning involves training a model on labeled data, where the correct outputs are provided during training. This method aims to predict the outcome based on input features and corresponding target variables. In contrast, unsupervised machine learning deals with unlabeled data, where the model must find patterns and relationships within the data without explicit guidance.

Real-world Application - Supervised Learning:

An example of supervised learning is spam email classification. By using a labeled dataset of emails (spam or not spam), a model can be trained to accurately classify incoming emails as either spam or legitimate based on their content and features.

Real-world Application - Unsupervised Learning:

One practical application of unsupervised learning is customer segmentation in marketing. By analyzing customer data without predefined categories, businesses can identify distinct groups of customers based on their purchasing behavior, preferences, and demographics to tailor marketing strategies more effectively.

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