How Support Vector Machine (SVM) Works Types of SVM Linear SVM Non-Linear SVM ML DL by Mahesh Huddar
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- Опубликовано: 4 окт 2024
- How Support Vector Machine (SVM) Works Types of SVM Linear SVM Non-Linear SVM Machine Learning Deep Learning by Mahesh Huddar
The following concepts are discussed:
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00:02 Support Vector Machine is a popular supervised learning algorithm for classification and regression problems.
01:22 SVM algorithms are used for classification problems in machine learning.
02:32 Selection of hyperplane depends on features and maximum margin
03:42 Support vectors are the nearest points to the hyperplane, and the maximum margin is used to select between them.
04:58 Linear SVM separates data with a straight line
06:06 Support vectors and hyperplane in SVM
07:20 Support Vector Machine (SVM) can create a hyperplane to maximize the distance from support vectors.
08:29 Support Vector Machine segregates data with a maximal margin.
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Perfect explanation I saw. Hats off.👏👏
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Beautifully explained
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Wonderful explanation
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Excellent - Very Nicely Explained - Keep up the Good Work.!!
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Thank you very clearly explained❤
Thank you very much sir,
But can in know the example manually clasification multiclass SVM in sentiment analytics?
sir, Nice explanation and thanks sir for such clear and simple explanation .
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Thank you so much sir....😊😊😊
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Thank you sir
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best video to understand svm
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Svm function with intercept some times considered positive and negative..while writing general expression..else excellent explanation
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Thanks you sir 🙏
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sir how to thank u for this beautiful explanation
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Thank you
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Nice explanation
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Great work. thanks!
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Hats off!
sir , video for soft and hard class svm
Thank you very much
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Thanks a lot.❤
You're welcome 😊
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Superbb
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really nice video
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sir can i get this ppt slides
Take screenshots
Go for gfg SVM Algorithms
Aasa ki haddu vundali gudda ku siggundali
No 😂😂
No 👎
Sir dmdw Lo kuda idhi rayocha???
Yes
Super
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Suggest books for practice
i have an exam in 2 hours
Bro send the question paper pls😅
Thank you sir
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Thank you sir
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