In this video, we explain the Support Vector Machine (SVM) algorithm in Machine Learning. You’ll learn what SVM is, how it works, the concept of hyperplanes, support vectors, and margins, and how SVM is used to separate data into different classes with maximum accuracy. We’ll also discuss linear vs non-linear SVM, kernel tricks (RBF, polynomial, sigmoid), and how SVM performs in ML projects.

By the end of this machine learning tutorial, you’ll know how to implement SVM in Python using scikit-learn and how it compares with other ml classifiers like Decision Trees and Logistic Regression.

👉 Following are the topics we covered in this Support Vector Machine in Machine Learning video:
00:00:00 Introduction
00:00:55 What is Support Vector Machine
00:03:42 Example
00:05:59 Hard margin classification
00:07:46 Soft margin classification
00:09:37 Regularization
00:10:30 IRIS data set
00:11:13 IRIS data set SVM
00:12:25 Non linear SVM
00:13:59 Polynomial Kernel
00:15:43 Kernel SVM
00:19:31 Use cases
00:22:01 SVM Project

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