Large matrices arise in many ML applications, including as representations of datasets, graphs, model weights, first and second-order derivatives, etc. Randomized Numerical Linear Algebra (RandNLA) is an area that uses randomness to develop improved algorithms for ubiquitous matrix problems. The area has reached a certain level of maturity, and current efforts of incorporating RandNLA algorithms into core numerical libraries, as well as recent advances in ML, Statistics, and Random Matrix Theory, have led to new theoretical and practical challenges. This tutorial will provide a self-contained overview of RandNLA in light of these important developments.