4. What are the differences between supervised and unsupervised learning?

Supervised and unsupervised learning are two fundamental types of machine learning, differing in how models are trained.

Supervised learning:

• The model learns from labeled data, where each training sample has an associated label.
• The goal is to predict labels for new data.
• Examples: classification (e.g., image recognition) and regression (e.g., price prediction).

Unsupervised learning:

• The model learns from unlabeled data, with the goal of discovering structure or patterns in the data.
• The goal is to identify hidden structures or group similar data.
• Examples: clustering (e.g., customer segmentation) and dimensionality reduction (e.g., PCA).

Main differences:

1. Training data: Supervised learning requires labeled data, while unsupervised learning uses unlabeled data.
2. Goal: Supervised learning focuses on predicting labels, while unsupervised learning focuses on discovering patterns.
3. Applications: Supervised learning is used in tasks requiring prediction, while unsupervised learning is used for data exploration and structure discovery.