Understanding the difference between supervised and unsupervised learning is foundational for anyone getting started in machine learning. Each type serves different purposes in extracting insights from data.
Supervised Learning
- Trained on labeled data
- Used for classification and regression
- Examples: spam detection, price prediction
Unsupervised Learning
Works with unlabeled data to find hidden patterns. Common use cases include clustering, anomaly detection, and dimensionality reduction.
Which One to Choose?
It depends on your dataset and problem type. Many real-world solutions involve a combination of both.
