# Introduction Kernel Density Estimation (KDE) is a non-parametric density estimation technique. In this lab, we will learn how to use KDE to generate new samples of data. We will use the scikit-learn library to implement this technique. ## 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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