Back to Mathematics for Machine Learning: Linear Algebra
Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London
12,232 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.
EC
Sep 9, 2019
Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.
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2426 - 2428 of 2,428 Reviews for Mathematics for Machine Learning: Linear Algebra
By Chris Y
•Aug 30, 2019
very bad, everything is not clear
By Enyang W
•Aug 23, 2019
worst course ever
By Vaibhav J
•Aug 10, 2020
Bad