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Overview
Artificial Intelligence and Data Science Algorithms in Python for Classification and Regression What you'll learn: Apply SVMs to practical applications: image recognition, spam detection, medical diagnosis, and regression analysisUnderstand the theory behind SVMs from scratch (basic geometry)Use Lagrangian Duality to derive the Kernel SVMUnderstand how Quadratic Programming is applied to SVMSupport Vector RegressionPolynomial Kernel, Gaussian Kernel, and Sigmoid KernelBuild your own RBF Network and other Neural Networks based on SVM Support Vector Machines (SVM) are one of the most...
Syllabus
- Welcome
- Beginner's Corner
- Review of Linear Classifiers
- Linear SVM
- Duality
- Kernel Methods
- Implementations and Extensions
- Neural Networks (Beginner's Corner 2)
- Setting Up Your Environment (FAQ by Student Request)
- Extra Help With Python Coding for Beginners (FAQ by Student Request)
Instructor
Lazy Programmer Inc.
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