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What is Dimensionality Reduction in Machine Learning about?

Module VI of CSC604 covers dimensionality reduction in machine learning, focusing on the curse of dimensionality, feature selection, and feature extraction techniques like Principal Component Analysis (PCA). It discusses the importance of reducing the number of features to improve model performance and avoid overfitting, as well as the various approaches to feature selection and extraction. The module highlights the advantages and disadvantages of dimensionality reduction methods, particularly emphasizing P

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yashsp20phpcomp

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EN