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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Suresh Kumar
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Hi Sir,
I want a clearity up on these
1. To learn Data Science "Machine learning" is part of it but we have to learn additionally python libraries (panda, numpy, matplotlib) or else in ML enough.
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Ayush Bharti
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how can i download the finaldata.csv?
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Jagannath Mahato
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Your way of teaching is very good. I thank you from my heart ❤️ that you are providing such good content for free.
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Muhammad Qasim
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Shafi Akhtar
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Aniket Kumar prasad
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Very helpful and easy to understand all the concepts, best teacher for learning ML.
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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
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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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