# Introduction This lab demonstrates how to use different kernels for Gaussian Process Regression (GPR) in Python's Scikit-learn library. GPR is a non-parametric regression technique that can fit complex models to data with noise. A kernel function is used to determine the similarity between any two input points. The choice of kernel function is important, as it determines the shape of the model that is fit to the data. In this lab, we will cover the most commonly used kernels in GPR. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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