## About Support Vector Machines

Link to Jupyter Notebook

This tutorial is adapted from Jake VanderPlas’s example of SVM as given in his reference book: Python Data Science Handbook

### Motivation for Support Vector Machines

We want to find a line/curve (in 2D) or a manifold (in n-D) that divides the class from each other. This is a type of `Discriminative Classification`

Consider a simple case of classification task, in which the two classes of points are well separated. We can find region in space which best separates the data into two classes. The `Support Vectors`

in Support Vector Machine are the (hyper)planes which lie at the edge of the individual classes. This idea is much easier to understand from 2D perspective.