Supervised learning is one type of Machine Learning where we have input variables (x) and an output variable (Y) and we use an algorithm to find the value of y by taking the value of X. Here y value may be a continuous value or categorical value.

Mathematically we can write: y=f(X)

Which represents the mapping function from the input to the output.

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

Supervise Machine Learning is categorized into regression and classification.

Supervised machine learning is a type of machine learning where an algorithm is trained on a labelled dataset, meaning a dataset that has input data (features) and corresponding output data (labels) provided. The goal of supervised learning is to use this labelled data to learn a mapping between the inputs and outputs, so that the algorithm can predict the output for new, unseen inputs.

During the training process, the algorithm learns to identify patterns in the input data and map those patterns to the corresponding output labels. Once the algorithm has been trained, it can be used to make predictions on new, unseen data, by applying the learned mapping to the input features.

Examples of applications of supervised machine learning include image classification, speech recognition, natural language processing, and fraud detection. Some popular supervised learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

What is Regression?

Regression analysis is a subfield of supervised machine learning. It aims to model the relationship between a certain number of features and a continuous target variable.

In general regression is categorized into Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression.