Question 1: What is the difference between supervised and unsupervised learning?
Answer: Supervised learning is a type of machine learning that uses labeled data to train a model. The model learns to map input data to output data by minimizing a loss function. Unsupervised learning, on the other hand, does not use labeled data. Instead, it learns to extract patterns and features from unlabeled data.
Explanation: Supervised learning is often used for tasks such as classification and regression, where the output data is a known value. Unsupervised learning is often used for tasks such as clustering and dimensionality reduction, where the output data is not known.
Question 2: What are some common supervised learning algorithms?
Answer: Some common supervised learning algorithms include linear regression, logistic regression, decision trees, and random forests.
Explanation: Linear regression is a simple but powerful algorithm that can be used for both classification and regression tasks. Logistic regression is a type of linear regression that is used for binary classification tasks. Decision trees are a type of supervised learning algorithm that can be used for both classification and regression tasks. Random forests are a type of ensemble learning algorithm that is built by combining multiple decision trees.
