<ul><li>Principal Component Analysis (PCA) is a tool for reducing dimensionality in data science.</li><li>t-SNE (t-Distributed Stochastic Neighbor Embedding) is an alternative to PCA that preserves local structure and captures complex, non-linear relationships in data.</li><li>t-SNE calculates similarities between data points and adjusts their positions in a lower-dimensional space iteratively to align with the original high-dimensional dataset.</li><li>This non-linear dimensionality reduction technique is useful for data analysis and retaining intrinsic relationships within the data.</li></ul>

One Step Above PCA: What is t-SNE?

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