What are Kernels in SVM? List popular kernels used in SVM along with a scenario of their applications.

Kernels in SVM are functions that transform input data into a higher-dimensional space to make it separable by a hyperplane. Common kernels include linear, polynomial, and radial basis function (RBF) kernels.

    - Applications:

      - Linear Kernel: Used when data is approximately linearly separable.

      - Polynomial Kernel: Useful when data has complex decision boundaries.

      - RBF Kernel: Suitable for data with no clear separation boundaries.