SVMs are a popular classification technique used in data science and machine learning.

In this video, I walk through how support vector machines work in a visual way, and then go step by step through how to write a Python script to use SVMs to classify muffin and cupcake recipes.

In Part 1a, I visually define the following terms:
Margin
Support vectors
Hyperplane

In Part 1b, I go through the following steps in a Jupyter Notebook:
Import libraries (pandas, numpy, sklearn, matplotlib)
Import data
Prepare the data
Fit the model
Visualize results
Predict a new case

In Part 2, I talk about ways to tune the model:
Higher dimensions
Multiple classes
C parameter
Kernel trick (RBF with gamma)

In Part 3, I talk about the pros and cons of SVM.

You can find all of my code and data on Github: https://github.com/adashofdata