R Tutorial: Exploring the MNIST dataset

Want to learn more? Take the full course at https://learn.datacamp.com/courses/advanced-dimensionality-reduction-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hello, this is Federico Castanedo and I will explain to you how to apply advanced dimensionality reduction techniques to solve exciting problems. I got a Phd in Artificial Intelligence and have many years of data science experience in academia, start-ups, and large corporations. Before we start, please remember this is an advanced course and we expect you have taken the previous course on Dimensionality Reduction. Let's begin with a brief introduction of the dimensionality reduction techniques that we will learn and then we will explore the MNIST dataset. In this course, we will learn how to apply two state-of-the-art dimensionality reduction techniques: t-SNE and Generalized Low Rank models. t-Distributed Stochastic Neighbor Embedding or t-SNE, is an algorithm that performs non-linear dimensionality reduction and we will explore how to use it in predictive models. On the other hand, we will also review GLRM, which is a parallelized optimisation algorithm that can be used with numerical and categorical variables and allows to impute missing values. Dimensionality reduction techniques are based on unsupervised machine learning algorithms and their application offers several advantages: it provides a way of doing feature selection; it compresses high dimensional data into a few important features; it saves memory and speeds up building machine learning models; it allows the visualisation of high dimensional datasets; and in the case of GLRM it also imputes missing data. In this course we will learn how to apply these dimensionality reduction techniques to exploit the mentioned advantages, using interesting datasets like the MNIST, a credit card fraud dataset from Kaggle and the fashion version of MNIST released by Zalando. Befo