Parametric vs non-parametric models

Parametric Models use a fixed number of parameters for creating the ML model.
They consider strong assumptions about the data.

For example, Linear regression, Logistic Regression, Naïve Bayes, Perceptron, etc.

The non-Parametric Model uses flexible numbers of parameters.
They only consider a few assumptions about the data.
However, these models are better for higher data and no prior knowledge.
For example, Decision Tree, K-Nearest Neighbour, SVM with Gaussian kernels, etc.