Principal Component Analysis, or PCA, is one of the minor miracles of machine learning. It’s a dimensionality-reduction technique that reduces the number of dimensions in a dataset without sacrificing a commensurate amount of information. While that might seem underwhelming on the face of it, it has profound implications for engineers and software developers working to build predictive models from their data. From visualizing high-dimensional data to performing real-time anomaly detection, PCA is a tool that should be in every machine-learning engineer’s toolbox. Learn what PCA in machine learning is, how it works, and more importantly, how to use it to solve real-world problems, with plenty of code samples to light the way.
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