What's the Difference?

Supervised learning uses labeled data to train models, while unsupervised learning works with unlabeled data to find hidden patterns. 🧠

Supervised Learning

  • Requires input-output pairs (e.g., images with labels)
  • Common use cases: classification, regression
  • Example: Predicting house prices from features
Supervised Learning

Unsupervised Learning

  • Finds structure in data without predefined labels
  • Common use cases: clustering, dimensionality reduction
  • Example: Grouping similar customer behaviors
Unsupervised Learning

For deeper exploration, check our Machine Learning Overview or Learning Types Guide. 📚

Data Analysis