202502012200
tags: #machine-learning #fundamentals

Supervised learning uses labeled training data to learn a mapping from inputs to outputs, while unsupervised learning finds patterns in data without labeled examples.

In supervised learning, we have input-output pairs (x, y) where the algorithm learns to predict y given x. This enables two main tasks: Regression vs Classification.

Unsupervised learning works with only inputs (x) and must discover hidden structure, such as clustering similar data points or reducing dimensionality.

The choice between supervised and unsupervised approaches depends on whether you have labeled data and what type of insights you want to extract from your dataset.

Reference

Machine Learning Yearning by Andrew Ng