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ML_Supervised
·
Module 6
Support Vector Machines from Scratch
Maximum Margin Classification and Support Vectors Explained
Explain the concepts of maximum margin classification and support vectors with visualizations.
2
Hinge Loss and Gradient for Linear SVM
Implement the hinge loss function and its gradient for a linear SVM.
3
Soft-Margin SVM with Gradient Descent
Implement a linear SVM with a soft margin using gradient descent.
4
Building Kernel Functions
Implement common kernel functions (polynomial and RBF) from scratch.
5
Kernelizing Your SVM for Non-linear Data
Integrate kernel functions into your SVM implementation to solve non-linear classification problems.
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Regularization Techniques
Next module
Decision Trees and Random Forests from Scratch