Artificial Intelligence & Machine Learning with Vector in Python

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Welcome to this course artificial intelligence & Machine Learning with Vector in Python for free Download


What you will learn in this Course:

  • Image recognition, spam detection, medical diagnosis, and regression analysis
  • Theory behind SVMs from scratch
  • Duality to derive the Kernel SVM
  • You Can Learn 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 are:

  • Logistic Regression,Regression Calculus, Linear Algebra, Probability
  • Python and Numpy coding


This course AI & Machine Learning with Vector in Python has been one that students have requested ever since I started making courses.

This course will cover the critical theory behind SVMs:

  • Linear SVM derivation
  • Quadratic programming (and Linear programming review)
  • Slack variables
  • Lagrangian Duality
  • Kernel SVM (nonlinear SVM)
  • Polynomial Kernels, Gaussian Kernels, Sigmoid Kernels, and String Kernels
  • RBF Networks (Radial Basis Function Neural Networks)
  • Support Vector Regression (SVR)
  • Multiclass Classification

Course content

  • Beginner’s Corner
  • Review of Linear Classifiers
  • Linear SVM
  • Duality
  • Kernel Methods
  • Implementations and Extensions
  • Neural Networks
  • Appendix

Who this the audience for the course:

  • 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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