**Support Vector Machines (SVMs)** are a popular type of supervised learning algorithm used for classification and regression tasks. Here’s a brief overview:

What is an SVM?

An SVM is a machine learning model that aims to find a hyperplane that maximally separates classes in a dataset. It does this by finding the hyperplane that maximizes the margin between classes.

Key Concepts:

1. Hyperplane: A decision boundary that separates classes.

2. Margin: The distance between the hyperplane and the closest data points (called support vectors).

3. Support Vectors: Data points that lie closest to the hyperplane and define the margin.

How SVMs Work:

1. Training: The algorithm takes labeled data as input and finds the optimal hyperplane that separates classes.

2. Classification: New, unseen data is classified by determining which side of the hyperplane it falls on.

Types of SVMs:

1. Linear SVM: Used for linearly separable data.

2. Non-Linear SVM: Used for non-linearly separable data, using kernel functions (e.g., polynomial, radial basis function).

Kernel Functions:

1. Linear Kernel: No transformation, used for linearly separable data.

2. Polynomial Kernel: Transforms data into a higher-dimensional space.

3. Radial Basis Function (RBF) Kernel: Transforms data into a higher-dimensional space using a Gaussian distribution.

Advantages:

1. Robust to noise: SVMs are robust to noisy data.

2. High-dimensional data: SVMs can handle high-dimensional data effectively.

3. Non-linear classification: SVMs can handle non-linear classification tasks using kernel functions.

Common Applications:

1. Image classification: SVMs are used in image classification tasks, such as object detection.

2. Text classification: SVMs are used in text classification tasks, such as spam filtering.

3. Bioinformatics: SVMs are used in bioinformatics for protein classification and gene expression analysis.