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:    • Support Vector Machines Part 2: The P...  
Part 3: The Radial (RBF) Kernel:    • Support Vector Machines Part 3: The R...  

NOTE: This StatQuest assumes you already know about...
The bias/variance tradeoff:    • Machine Learning Fundamentals: Bias a...  
Cross Validation:    • Machine Learning Fundamentals: Cross ...  

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/garet...

I also found this blogpost helpful for understanding the Kernel Trick: https://blog.statsbot.co/supportvect...

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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