SVM Regression, prediction and losses

In this 1-hour long project-based course, you will learn how to
Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally
pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning.
Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your understanding to thoroughness in machine learning.

• Project Overview
• In this 1-hour long project-based course, you will learn how to
  Train SVM regression model- with large & small margin, second degree polynomial kernel, make prediction using Linear SVM classifier; how a small weight vector results in a large margin? and finally
  pictorial representation for Hinge loss. This project gives you easy access to the invaluable learning techniques used by experts in machine learning.
  Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your understanding to thoroughness in machine learning.