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Core
Dimensionality Reduction: PCA, t-SNE, UMAP
Dimensionality reduction compresses high-dimensional data into fewer axes. PCA is a linear projection that maximizes retained variance and is reversible enough to feed downstream models. t-SNE and UMAP are nonlinear methods that preserve local neighborhood structure for 2D or 3D visualization only. Applied-AI interviews probe whether you know that t-SNE and UMAP distort global geometry, why you never cluster on their coordinates, and how the curse of dimensionality motivates the whole exercise.
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PRACTICE THIS IN REAL QUESTIONS
Machine Learning & Data ScienceCompare dimensionality reduction methods: PCA vs t-SNE vs UMAP.→Machine Learning & Data ScienceExplain PCA and the curse of dimensionality. When and how do you reduce dimensions?→Machine Learning & Data ScienceWhat is Linear Discriminant Analysis (LDA), and how does it differ from PCA?→Machine Learning & Data ScienceWhat is the curse of dimensionality, and how does it affect ML?→Coding & DSAImplement PCA from scratch via SVD: center the data, project onto top components, report variance.→Machine Learning & Data ScienceFor a dataset with a million points, would you use a deep network or KNN, and why?→
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