# What is Supervised Learning?

## Introduction to supervised learning in machine learning

well if you are a **beginner** in the field of **machine** **learning** then most probably you have heard of these types of learning well if don’t then don’t worry I would be explaining to you what is **supervised** **learning** in this article

# What is supervised learning?

**Supervised learning** involves learning a** function that maps **an** input **to an **output **based on example** input-output pairs. **by the definition supervised learning means this.

# How supervised learning works?

in **supervised** **learning**, first, the model **train** itself on a **training** labeled **dataset** then after the **training** completes the model is now **trained** and got an experience from the **past** dataset values and stores all the value in the **model** itself so now if we get a **test** data or a data that we want to **predict**. the model predicts an outcome on the **trained** **data**.

# Types of models in supervised learning

## Regression models

**regression models **are **continuous** and they built a **correlation** with the dataset and the model try to use a **mathematical** **function** for eg it can use either a **straight** **line** or a **curve** to fit the **dataset** and the **output** in these types of model can be a **number** as you can see in the **image** below

## Classification models

on the other hand, we have **classification** **models** in classification models we **categorize** into many **classes** with the help of **algorithms** the algorithms it uses are a **straight** **line** or curve to **differentiate** between the data into **classes** the output can be **binary** or **categorical **as you can see in the image below

# Models in supervised learning

- Simple Linear Regression
- Multiple Linear Regression

- Polynomial Regression

- Logistic Regression

- Decision Tree

- Random Forest

- Support Vector Models

- K-Nearest Neighbours
- Kernel SVM
- Naïve Bayes

# Applications of supervised learning

- fraud detection
- market forecasting
- Image- and object-recognition
- Customer sentiment analysis

## what’s next?

well I have good news for you I would be bringing some **more articles** to **explain machine learning models with codes** so leave a **comment** and tell me how excited are you about this