Mathematics in Machine Learning: Linear Algebra

In this linear algebra course, we'll look at what linear algebra is and how it relates to vectors and matrices. Then we'll look at what vectors and matrices are and how to work with them, including the tricky problem of eigenvalues ​​and eigenvectors, and how to use them to solve problems. Finally, we'll look at how to do fun things with data sets with them - like how to rotate images of faces and how to extract eigenvectors to see how the Pagerank algorithm works. Since we're targeting data-driven applications, we'll be implementing some of these ideas in code rather than just pencil and paper. Towards the end of the course, you'll be writing code blocks and running Jupyter notebooks in Python, but don't worry, they'll be fairly short, focused on concepts, and will help you along if you've never done any coding before. By the end of this course, you will have an intuitive understanding of vectors and matrices that will help you bridge the gap in solving linear algebra problems and apply these concepts to machine learning.