# Introduction to Support Vector Machine

Get introduced to the support vector machine.

## We'll cover the following

- Introduction to regression vs. classification
- Support vector machine
- How does the SVM classify?
- The maximum margin hyperplane
- Why maximize the margin?
- SVM origins: The perceptron algorithm
- Finding the maximum margin
- Hinge loss and non-linearly separable cases
- Hinge loss and slack
- C: The regularizing hyperparameter

We use a best-fitted line to predict a continuous target in regression-based algorithms like linear regression.

What if we try to use the linear regression algorithm to predict some classes (for example, 0/1)? That isn’t a good idea (we typically convert the categorical targets or labels to integer classes 0/1).

To accomplish a classification task, we consider the line as a boundary that splits the space instead of fitting the points.

Regression vs. classification

Get hands-on with 1200+ tech skills courses.