# Introduction In this lab, we will compare different Manifold Learning algorithms to perform non-linear dimensionality reduction. The purpose of this is to reduce the dimensionality of the dataset while preserving the original data's essential features. We will be using the S-curve dataset, which is a commonly used dataset for dimensionality reduction. We will use algorithms like Locally Linear Embeddings, Isomap Embedding, Multidimensional Scaling, Spectral Embedding, and T-distributed Stochastic Neighbor Embedding. ## 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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