Support Vector Machines A Visual Explanation with Sample Python Code

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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

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