Back to Mathematics for Machine Learning: Linear Algebra
Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London
12,430 ratings
About the Course
In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before.
At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.
Top reviews
PL
Aug 25, 2018
Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.
MG
May 30, 2020
A great course covering key aspects of Linear Algebra with the lecturers giving a good intuition about what's going on with the subjects.(PS. I loved the sense of humor of Prof.Dye it was really fun!)
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