Machine Learning And Ai: Support Vector Machines In Python

Machine Learning And Ai: Support Vector Machines In Python
Last updated 5/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

What you’ll learn
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

Requirements
Calculus, Matrix Arithmetic / Geometry, Basic Probability
Python and Numpy coding
Logistic Regression

Description
Who this course is for:
Beginners who want to know how to use the SVM for practical problems,Experts who want to know all the theory behind the SVM,Professionals who want to know how to effectively tune the SVM for their application

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