Supervised Learning

Supervised Learning is a type of machine learning where the algorithm is trained on a labeled dataset, consisting of input-output pairs. The goal is to learn a mapping function from inputs to outputs, allowing the model to make accurate predictions on new, unseen data. Common tasks within supervised learning include regression, where the output is a continuous value, and classification, where the output is a discrete label.

Regression

Regression is a type of supervised learning that deals with predicting continuous values. It involves finding the relationship between input variables and a continuous target variable, allowing the model to make predictions on new data.

A few applications of regression include:

• Stock price prediction

• Weather forecasting

• Agricultural yield forecasting 

• Property price prediction

• Performance prediction

Classification

Classification is a type of supervised learning focused on predicting the category or class label of input data. The algorithm learns from labeled examples and generalizes its knowledge to classify unseen instances into predefined categories.

A few applications of classification include:

• Email spam detection

• Default prediction

• Fraud detection

• Disease diagnosis

• Face recognition