ML & Dimensionality Reduction: Performing Principal Component Analysis

Principal component analysis (PCA) is a must-know pre-processing technique for anyone working with machine learning (ML). Used to process data fed into ML models, PCA is useful in many scenarios, such as exploratory data analysis, dimensionality reduction, and latent feature extraction.

Use this course to learn the basic intuition behind principal component analysis along with how to use PCA. Start by visualizing how principal components work. Then, examine how they can be computed mathematically using the eigenvectors and eigenvalues of the covariance matrix of the data.

As you advance, manually compute principal components, view the re-oriented data, and compare this result with the principal components computed. Lastly, use PCA for dimensionality reduction to train a classification model.

When you're done, you'll have the skills and knowledge to use PCA to build more robust machine learning models.