What is the difference between supervised and unsupervised learning

Short answer

Supervised learning uses labeled input-output pairs to train models for prediction, while unsupervised learning finds patterns in unlabeled data through clustering and dimensionality reduction. To evaluate model quality, see how to evaluate machine learning model performance. For building models from scratch, see how to build a neural network from scratch.

Steps

1. Identify if your dataset has target labels
2. If labeled, choose supervised algorithms: regression or classification
3. If unlabeled, choose unsupervised algorithms: k-means, PCA
4. Preprocess data accordingly
5. Evaluate results with appropriate metrics

Tips

• Supervised learning requires more data preparation but is easier to evaluate
• Unsupervised learning is useful for exploratory data analysis
• Understand the bias-variance tradeoff in machine learning for both approaches