Machine Learning and AI: Support Vector Machines in Python

• Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
• Understand the theory behind SVMs from scratch (basic geometry)
• Use Lagrangian Duality to derive the Kernel SVM
• Understand how Quadratic Programming is applied to SVM
• Support Vector Regression
• Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
• Build your own RBF Network and other Neural Networks based on SVM 

This course will cover the critical theory behind SVMs:

• Linear SVM derivation
• Hinge loss (and its relation to the Cross-Entropy loss)
• Quadratic programming (and Linear programming review)
• Slack variables
• Lagrangian Duality
• Kernel SVM (nonlinear SVM)
• Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
• Learn how to achieve an infinite-dimensional feature expansion
• Projected Gradient Descent
• SMO (Sequential Minimal Optimization)
• RBF Networks (Radial Basis Function Neural Networks)
• Support Vector Regression (SVR)
• Multiclass Classification