Support vector machine (SVM) is a popular classification algorithm. This tutorial covers some theory first and then goes over python coding to solve iris flower classification problem using svm and sklearn library. We also cover different parameters such as gamma, regularization and how to fine tune svm classifier using these parameters. Basically the way support vector machine works is it draws a hyper plane in n dimension space such that it maximizes the margin between classification groups.

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Code: https://github.com/codebasics/py/blob/master/ML/10_svm/10_svm.ipynb

Exercise: Open above notebook from github and go to the end.

Exercise solution: https://github.com/codebasics/py/blob/master/ML/10_svm/Exercise/10_svm_exercise_digits.ipynb

Topics that are covered in this Video:
0:00 Introduction
0:20 Theory (Explain support vector machine using sklearn iris dataset flower classification problem)
3:11 What is Gamma?
4:21 What is Regularization?
5:27 Kernel
6:32 Coding (Start)
18:08 sklearn.svm SVC
21:41 Exercise (Classify hand written digits dataset from sklearn using SVM)

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Machine Learning Tutorial Python – 11 Random Forest: https://www.youtube.com/watch?v=ok2s1vV9XW0&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=12

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