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Supervised vs. Unsupervised Learning

The focus keyword for this section is "supervised vs. unsupervised learning."

Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data. The algorithm learns to map input data to the correct output based on the labels provided during training.

Example of supervised learning includes:

• Classification: Predicting whether an email is spam or not spam based on previous labeled emails.
• Regression: Predicting the price of a house based on features like size, location, and number of bedrooms.

Unsupervised Learning

In unsupervised learning, the model is trained on unlabeled data. The algorithm tries to find patterns and relationships in the data without explicit feedback.

Example of unsupervised learning includes:

• Clustering: Grouping customers based on their purchasing behavior without any predefined categories.
• Dimensionality Reduction: Reducing the number of features in a dataset while preserving most of its information.

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