Predictive Analytics and Machine Learning Module 3: Dimensionality Reduction (Synchronous e-Learning)

This is one of two modules where participants will learn how to execute unsupervised machine learning. Participants will learn the essential concepts of dimensionality reduction, and why and when it is needed in order to solve certain types of questions. They will learn how to use a cutting-edge Tidymodels framework in the R data science ecosystem when executing Principal Component Analysis or PCA (linear) and Uniform Manifold Approximation and Projection or UMAP (nonlinear), interpreting the results of dimensionality reduction. The course will guide participants to apply Principal Component Analysis (PCA) to solve real-world business problems (e.g., customer segmentation and asset allocation). The course will also discuss the potential use of Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) in the processes of supervised learning.

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