There are six types of kernels in SVM :
Linear kernel : used when data is linearly separable.
Polynomial kernel : When you have discrete data that has no natural notion of smoothness.
Radial basis kernel : Create a decision boundary able to do a much better job of separating two classes than the linear kernel.
Sigmoid kernel : used as an activation function for neural networks.
