Support Vector Machines Part 1 (of 3): Main Ideas!!!

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 Polyno...   Part 3: The Radial (RBF) Kernel:    • Support Vector Machines Part 3: The Radial...   NOTE: This StatQuest assumes you already know about... The bias/variance tradeoff:    • Machine Learning Fundamentals: Bias and Va...   Cross Validation:    • Machine Learning Fundamentals: Cross Valid...   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/support-vect... For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon:   / statquest   ...or... YouTube Membership:    / @statquest   ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:   / joshuastarmer   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