This course focuses on dimensionality reduction techniques, which are based on unsupervised machine learning algorithms. These techniques offer numerous advantages, and this course will teach you how to effectively apply them. Throughout the course, you will work with interesting datasets such as the MNIST database of handwritten digits, the fashion version of MNIST released by Zalando, and a credit card fraud detection dataset. The course begins by introducing t-SNE, an algorithm that performs non-linear dimensionality reduction. You will gain a comprehensive understanding of this algorithm and its applications. Additionally, you will explore various characteristics of dimensionality reduction that can be applied in predictive models. Towards the end of the course, you will learn about the application of GLRM, a technique used to compress big data that contains both numerical and categorical values. You will also discover how GLRM can be used to impute missing values. By the end of this course, you will be equipped with the knowledge and skills to effectively compress high dimensional data. Are you ready to get started?