This document provides a step-by-step introduction to performing a first step support vector machine (SVM) classification. It outlines 5 key steps: 1) plotting the initial data points, 2) defining the border between classes as a perpendicular line between two points, 3) determining the distance between each data point and candidate border lines, 4) selecting the border line with the lowest average distance to points, 5) plotting the final border line. Code for implementing this first step SVM approach can be found at a provided GitHub URL.