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What is Dimensionality Reduction Techniques in ML about?

The document outlines the syllabus for a Machine Learning course focusing on Dimensionality Reduction techniques, including PCA, Kernel PCA, and feature selection methods. It discusses the challenges posed by high-dimensional data, known as the curse of dimensionality, and various approaches for reducing dimensionality such as feature selection, feature extraction, and projection methods. Additionally, it details the steps involved in the Backward Elimination technique for feature selection.

Author

rajawatabhinav4

Language

EN