Practical Data Science using Python - Principal Component Analysis - Concepts

The video tutorial introduces Principal Component Analysis (PCA) as a technique for dimensionality reduction, crucial for handling high-dimensional data in machine learning. It covers the importance of data standardization, the role of covariance matrices, eigenvectors, and eigenvalues in PCA, and how to recast data using principal components. The tutorial also discusses the challenges of high-dimensional data, such as the curse of dimensionality, and the benefits of reducing dimensions for model accuracy and visualization.

What challenges arise when dealing with high-dimensional data?

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How can PCA improve the accuracy of machine learning models?

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Discuss the difference between feature selection and feature extraction.

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What is the significance of retaining variance information in PCA?

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