Supervised Learning

Supervised Learning is one of the most common types of machine learning, where the model is trained on a labeled dataset. This means that each input data point is paired with the correct output, allowing the algorithm to learn the relationship between the input features and the target variable.

In supervised learning, the goal is to make accurate predictions on new, unseen data. The two main tasks are:

Classification: Predicting discrete labels (e.g., spam vs. non-spam emails).
Regression: Predicting continuous values (e.g., house prices, stock prices).
Popular algorithms in supervised learning include:

Linear Regression for regression tasks.
Logistic Regression for binary classification.
Decision Trees and Random Forests for both classification and regression.
Support Vector Machines (SVM) for high-dimensional classification problems.

Supervised learning is widely used in applications such as fraud detection, medical diagnostics, and sentiment analysis.