In this article, we will talk about the types of kernels used in machine learning to separate non-linear problems data using a linear classifier. The raw data need extra care to get insights and representation from it, the features in the data can be in any pattern and we need to search for the relation between them. The pattern analysis and relation can be in clusters, correlations, classifications, rankings, etc. Kernel Trick: The study of different kernels uses a kernel function that works in high dimensional space without considering the coordinates of the data. So, this type of process/approach is computationally cheaper and effective. As the name suggests, the kernel separates the data linearly as it is the one-dimensional kernel.