# Introduction Gaussian process regression is a statistical modelling technique used to predict the outcome of a target variable based on input variables. The technique models the distribution of the target variable as a Gaussian process, which is a collection of random variables, any finite number of which have a joint Gaussian distribution. The technique is particularly useful in cases where the relationship between the input and target variables is non-linear. In this lab, we will learn how to use Gaussian process regression with noise-level estimation in Python, using the scikit-learn library. ## 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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