Advanced Dimensionality Reduction in R

To reduce dimensionality, unsupervised machine learning algorithms can be used. These algorithms have many benefits. You will learn how to reduce dimensionality to get the most out of these benefits. This course uses interesting datasets like the MNIST database with handwritten numbers, Zalando's fashion version MNIST and a credit card fraud detection dataset. We will first be examining t–SNE, which is an algorithm that performs linear dimensionality loss. Next, we'll show you how to reduce dimensionality in predictive models. Finally, you will learn how GLRM can be used for compressing large data (with numerical and categorical values) and to impute missing value. Are you ready to start compressing high dimensional data?