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Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysis
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Understand the theory behind SVMs from scratch (basic geometry)
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Use Lagrangian Duality to derive the Kernel SVM
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Understand how Quadratic Programming is applied to SVM
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Support Vector Regression
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Polynomial Kernel, Gaussian Kernel, and Sigmoid Kernel
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Build your own RBF Network and other Neural Networks based on SVM
- This event has passed.
Jun
14
Machine Learning and AI: Support Vector Machines in Python
June 14, 2023
$25
