Uploaded September 2019 | Updated September 2026, 1 week ago

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Support Vector Machines are one of the most mysterious methods in Machine Learning. This StatQuest sweeps away the mystery to let know how they work.

Part 2: The Polynomial Kernel: youtu.be/Toet3EiSFcM
Part 3: The Radial (RBF) Kernel: youtu.be/Qc5IyLW_hns

NOTE: This StatQuest assumes you already know about...
The bias/variance tradeoff: youtu.be/EuBBz3bI-aA
Cross Validation: youtu.be/fSytzGwwBVw

ALSO NOTE: This StatQuest is based on description of Support Vector Machines, and associated concepts, found on pages 337 to 354 of the Introduction to Statistical Learning in R: http://faculty.marshall.usc.edu/gareth-james/ISL/

I also found this blogpost helpful for understanding the Kernel Trick: blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93

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0:00 Awesome song and introduction
0:40 Basic concepts and Maximal Margin Classifiers
4:35 Soft Margins (allowing misclassifications)
6:46 Soft Margin and Support Vector Classifiers
12:23 Intuition behind Support Vector Machines
15:25 The polynomial kernel function
17:30 The radial basis function (RBF) kernel
18:32 The kernel trick
19:31 Summary of concepts



#statquest #SVM