Lec-6.28 Support Vector Machines Part-I (Arif Butt @ Data Science)
00:55 Learning Agenda
02:21 An Overview of SVM
05:10 Linearly Separable Data
07:09 Hyperplanes as Decision Boundary
08:25 How to select the best Decision Boundary
10:41 Geometric Intuition of How SVM work?
14:58 Equation of a Line/Hyperplane
19:54 Classifying a new Datapoint using SVM
24:35 Positive and Negative Hyperplanes
32:23 Decision Rule for SVM (Optimization Function)
35:08 Formula to Calculate the margin
39:28 Coding Example: Hard. Margin SVM
email: [email protected], [email protected], [email protected]
Lecture Slides and Resources: http://arifbutt.me
Jupyter notebooks: https://github.com/arifpucit/data-sci...