The Support Vector Machine, or SVM, is a linear model that can be used to solve classification and regression issues. It can solve both linear and nonlinear problems and is useful for a wide range of applications. SVM is a basic concept: The method divides the data into classes by drawing a line or hyperplane.
The name SVM, or Support Vector Machine, is well-known among those who work in Machine Learning or Data Science. SVR, on the other hand, is not the same as SVM. As the name implies, SVR is a regression algorithm, which means we can use it instead of SVM for working with continuous values.
The support vector machine method's purpose is to find a hyperplane in an n-dimensional space, where n is the number of features or independent variables. Let me give you an example; we're using classification as an example since it's how data is classified in the vast majority of cases.
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Rishu Shrivastav
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
5
very nicely explained
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Vrushali Kandesar
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Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
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Amazing Teaching
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Juboraj Juboraj
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Easy to understand & explain details.
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
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Kushal is very good explainer he is covering all topics nicely 👍
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