Back to Mathematics for Machine Learning: Linear Algebra
Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London
12,095 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
HE
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the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering
NS
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Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.
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2401 - 2404 of 2,404 Reviews for Mathematics for Machine Learning: Linear Algebra
By Andrew E
•Jun 17, 2019
First quiz doesn’t work properly. Can’t submit.
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