Dimensionality Reduction with PCA in Python | Scikit-Learn Tutorial
In this video, you will learn Dimensionality Reduction using Principal Component Analysis (PCA) in Machine Learning.
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PCA is one of the most important techniques in data science and machine learning used to reduce the number of features while keeping the most important information.
In this tutorial we cover:
• What is Dimensionality Reduction
• How Principal Component Analysis (PCA) works
• PCA example using Iris Dataset
• Implementing PCA in Python with Scikit-Learn
• Understanding variance and principal components
• PCA vs TruncatedSVD for sparse matrices
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