Back to Mathematics for Machine Learning: Linear Algebra
Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London
12,533 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
DV
Jun 24, 2019
This was a terrific course; the instructors' are passionate and knowledgeable about the course material, the assignments are engaging and relevant, and the length of the videos feels "just right".
LK
Oct 26, 2023
Very good course. I liked very much the way the topics were presented and explained. I especially appreciate David Dye's clarity of explanations, enthusiasm, passion, and joyful attitude. Thank you.
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2476 - 2477 of 2,477 Reviews for Mathematics for Machine Learning: Linear Algebra
By Enyang W
•Aug 23, 2019
worst course ever
By Vaibhav J
•Aug 9, 2020
Bad