Skill

Dimensionality Reduction

Data Science, Analytics and AI/ML

Dimensionality reduction is a set of techniques in machine learning and statistics used to reduce the number of variables in a dataset while preserving as much meaningful information as possible, using methods like Principal Component Analysis (PCA), t-SNE, or UMAP. Data scientists use it to simplify complex datasets, speed up model training, reduce noise, and visualize high-dimensional data in two or three dimensions.

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• Feature Engineering
• Machine Learning
• Feature Selection
• Principal Component Analysis
• Autoencoders
• Unsupervised Learning