Dimensionality Reduction in ML | PCA vs t-SNE vs UMAP | Unsupervised Learning | Ch 5 – Pt 2
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Soledad Galli | Data Scientist on Instagram: "Ever wondered how algorithms like UMAP turn complex, high-dimensional data into something we can see in 2D or 3D? 🌌 It works by preserving the key relationships and patterns between data points, capturing the shape (or manifold) your data creates in higher dimensions. In our Clustering & Dimensionality Reduction course, you’ll learn how this magic happens step-by-step from theory to Python implementation. 🧠💻 🎓 Learn more - link in bio/comments. #
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AI | Machine Learning | Tech on Instagram: "UMAP (Uniform Manifold Approximation and Projection) is a dimensionality reduction technique used to simplify complex, high-dimensional data—making it easier to visualize in just 2 or 3 dimensions. Imagine you have a dataset that is difficult to understand since there are many features (such as height, age, weight, etc.). UMAP projects this data for visualization, in a 2D or 3D plot for instance. UMAP works by maintaining key spatial features and hiera
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