Support Vector Machine (SVM): SVM can be used for Linear and Non-Linear Data. Uses: Classification (SVC), Regression (SVR), Outlier Detection. It is mostly used for Classification tasks. Applications: Image Classification, Categorization (text classification), Protein Classification, Handwriting Classification. 3 Important things in SVM are: Hyper Plane, Support Vectors, Margins

I learned about Support Vector Machine (SVM): SVM can be used for Linear and Non-Linear Data. mostly use for classification task

I learned about Support Vector Machine (SVM): SVM can be used for Linear and Non-Linear Data. Uses: Classification (SVC), Regression (SVR), Outlier Detection. It is mostly used for Classification tasks. Applications: Image Classification, Categorization (text classification), Protein Classification, Handwriting Classification. 3 Important things in SVM are: Hyper Plane, Support Vectors, Margins

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Done

I learned to Support vector machine (Hyperplane, Support vector, and Margins).

I learned in this lecture support vector machine (SVM) (hyperplane, support vector, margins).

In the support vector machine, every line shows the degree of data points and three types of kernel functions that can be used to separate the data points through hyperplane boundaries. (1) Linear Kernel (2) RBF-Radial Basis Function Kernel (3) Polynomial Kernel

AOA,I learned in this lecture about SUPPORT VECTOR MACHINE ( S.V.M ) and its types TYPES OF SUPPORT VECTOR MACHINE ( S.V.M ) ( apply for linear and nonlinear data )1-Linear Support Vector Machine (SVM) 2-Polynomial Support Vector Machine 3-Radial Basis Function (RBF) Support Vector Machine And also learned about applications of SVM, which are1: Image classification 2: Classification for text 3: Protein classification 4: Handwriting And also learned about1: Hyper plane 2; Support vector 3: Margins ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey Ameen.