Principal Component Analysis for Dimensionality Reduction
In this lesson, you'll learn about Principal Component Analysis, which is a famous dimensionality reduction technique to help represent data in lower dimensions which can be helpful in visualizations and modeling.
We'll cover the following...
Principal Component Analysis
PCA stands for Principal Component Analysis. It helps us transform high-dimensional datasets (having a large number of features) into a low-dimensional one (having a smaller number of features) without losing too much information. These datasets can include images or simple structured datasets. This helps us deal with the curse of dimensionality, which results in complex ...
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