Machine Learning Tutorial 7 - Support Vector Machines (SVM) in Scikit-learn

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In this video, we'll implement Support Vector Machines using SciKit-Learn Library!

SVM are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

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Sir god bless you. May god grant you a happy life.

thevoidwalkertvw

This is great. Many of the other tutorials I've seen go into too much detail for what I need but this explained the process very clearly and concisely. Thank you 10/10

crazymexicandope

Very straight-forward and clear tutorial, thanks a lot!

duggy

4:37 - Little correction - head is a function so, don't forget the parentheses after head :)

DarshanSenTheComposer

Is there any way to define learning rate and number of epochs to run and also get the Validation loss, Validation Accuracy for each epoch?

rajdeepjadhav

sir, If I give kernel=linear, it is not showing the output, kindly tell me why is it so?

grac

Machine Learning Tutorial 7 - Support Vector Machines (SVM) in Scikit-learn

Machine Learning Tutorial 7 - Support Vector Machines (SVM) in Scikit-learn

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