What kernel can be used in SVM to handle non-linear data?Answer areaLinear KernelPolynomial KernelRadial Basis Function (RBF) KernelAll of the above

All of the above. In Support Vector Machine (SVM), different types of kernels can be used to handle non-linear data. These include:

1. Linear Kernel: This is used when the data is linearly separable. It is one of the most common kernels to be used. It is represented by the dot product of the two inputs.

2. Polynomial Kernel: This is a more generalized form of the linear kernel and can distinguish curved or nonlinear input space.

3. Radial Basis Function (RBF) Kernel: This is a popular kernel function commonly used in SVM classification. It can map an input space in infinite dimensional space.

So, all of the above-mentioned kernels can be used in SVM to handle non-linear data.