# Introduction Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of a dataset while preserving most of its original variation. However, PCA is a linear method and may not work well when the data has a non-linear structure. In such cases, Kernel PCA can be used instead of PCA. In this lab, we will demonstrate the differences between PCA and Kernel PCA and how to use them. ## 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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