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Topics To Learn In Support Vector Machine.
Topics To Learn In SVM Table Of Contents: Introduction to SVM What is SVM? Use cases and applications of SVM Strengths and weaknesses of SVM Mathematical Foundations Linear separability Concept of a hyperplane Margin and margin maximization Support vectors and their role Functional and geometric margins SVM for Linearly Separable Data Objective function for linear SVM Hard margin SVM Optimization problem formulation Lagrange multipliers and the dual problem SVM for Non-Linearly Separable Data Soft margin SVM Slack variables and their significance Trade-off parameter CCC: bias-variance tradeoff Practical scenarios for soft margin SVM Kernel Trick What is the kernel trick? Common
