In machine learning, there are mainly two types of models, Parametric and Non-parametric. Here parameters are the predictor variables that are used to build the machine learning model. The explanation of these models is given below:

Parametric Model: The parametric models use a fixed number of the parameters to create the ML model. It considers strong assumptions about the data. The examples of the parametric models are Linear regression, Logistic Regression, Naïve Bayes, Perceptron, etc.

Non-Parametric Model: The non-parametric model uses flexible numbers of parameters. It considers a few assumptions about the data. These models are good for higher data and no prior knowledge. The examples of the non-parametric models are Decision Tree, K-Nearest Neighbour, SVM with Gaussian kernels, etc.