The goal of the SVM algorithm is to find a hyperplane in an N-dimensional space that categorises data points clearly. The hyperplane's size is determined by the number of features. If there are only two input characteristics, the hyperplane is merely a line.
SVM is a supervised machine learning technique that can be used to solve problems like classification and regression. It transforms your data using a technique known as the kernel trick, and then calculates an ideal boundary between the available outputs based on these alterations.
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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
4
how can i download the finaldata.csv?
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Jagannath Mahato
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Hello Kushal Sir!
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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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
Extremely helpful and I recommend to my peer as well for this course.
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Shafi Akhtar
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Aniket Kumar prasad
5
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
5
very nicely explained
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Vrushali Kandesar
5
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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