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: Part 3: The Radial (RBF) Kernel: NOTE: This StatQuest assumes you already know about... The bias/variance tradeoff: Cross Validation: 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: I also found this blogpost helpful for understanding the Kernel Trick: For a complete index of all the StatQuest videos, check out: If you'd like to support StatQuest, please consider... Patreon: ...or... YouTube Membership: ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... ...or just donating to StatQuest! Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: 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