# Introduction In this lab, we will explore manifold learning, which is an approach to non-linear dimensionality reduction. Dimensionality reduction is often used to visualize high-dimensional datasets, as it can be difficult to interpret data in more than three dimensions. Manifold learning algorithms aim to find a lower-dimensional representation of the data that preserves the underlying structure. In this lab, we will use the scikit-learn library to perform manifold learning on various datasets. We will explore different algorithms and compare their performance and outputs. ## 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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