Hi there, I'm David Dye, and welcome to the Mathematics for Machine Learning specialization. Before we get stuck in, let's set the scene. Machine learning is a set of powerful mathematical tools that enable us, to represent, interpret, and control the complex world around us. However, even just the word mathematics makes some people feel uneasy and unwelcome to explore the topic. The purpose of this specialization is to take you on a tour through the basic maths underlying these methods, focusing in particular on building your intuition rather than worrying too much about the details. Thanks to the amazing machine learning community, it's actually possible to apply many powerful machine learning methods without understanding very much about the underpinning mathematics, by using open source libraries. This is great, but problems can arise and without some sense of the language and meaning of the relevant maths, you can struggle to work out what's gone wrong or how to fix it. The ideal outcome of this specialization is that it will give you the confidence and motivation to immediately dive into one of the hundreds of boolean applied machine learning courses already available online, and not be intimidated by the matrix notation or the calculus. We want to open up machine learning to as many people as possible, and not just leave all the fun to computer scientists. This first course offers the introduction to linear algebra which is essentially a set of notational conventions and handy operations, that allow you to manipulate large systems of equations conveniently. Over the next five modules, we'll be focusing on building your intuition about vectors and translations through the use of quizzes and interactive widgets as well as occasionally asking you to fill in the gaps in some Python coding examples. In the final module, Dr. Sam Cooper will bring it all together by showing you how linear algebra is at the heart of Google's famous page rank algorithm, which is used for deciding the order of web pages in search results. Hopefully, if you find this course useful, you'll stick around for a follow-on course by Sam and I who will introduce you to multivariate calculus. Then, in our other course Dr. Mark Dyes and I will introduce principal component analysis. So welcome. We really hope that the course will be productive and useful for you but also quite a lot of fun and I look forward to hearing from you in the forums.