CourseraArtificial IntelligenceFree courseBeginner levelCertificate

Mathematics for Machine Learning: Linear Algebra

4.8

498 reviews

Created by: David Dye

Produced in 2018

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Overall Score : 96 / 100

Course Description

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.

Beginner Level

Time: 4h

Instructor Details

David Dye

4.8 Rating
498 Reviews

David Dye is a Professor of Metallurgy in the Department of Materials. He develops alloys for jet engines, nuclear and caloric materials so as to reduce fuel burn and avoid in-service failure. This involves crystallography (vectors and transformation matrices) and techniques like neutron and synchrotron X-ray diffraction and electron microscopy at the atomic scale. These give rise to 'big data' analysis problems associated simply with the amounts of data we can now collect. His Phd and undergraduate degrees were from Cambridge University in 1997 and 2000; he joined Imperial in 2003. He also teaches introductory mathematics - errors and data analysis, and has won student-led awards for innovation in teaching.

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4.8

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By Francisco R A on 17-Oct-19

I came to this course after starting other ML courses feeling the need to refresh/update the mathematical foundations to follow those previous courses. I have really enjoyed it and think of it as a great course in general.Having read some other opinions here I find it a little bit odd to read people complaining about the python tasks. If you come to a course like this one is because you are interested in ML so python is something you will surely be using, so learning a bit before engaging this course would be a first step.Regarding the maths, this course doesn't go in depth in maths theorems and stuff like that, it explains in a visual way what you need and then use the maths to accomplish it.

By Michael R on 17-Sep-19

Very visual and engaging. The lecturers' passion for the topic adds to the learning experience

By SiriusXDJ on 15-Sep-19

A very useful course.

By Wang Z on 15-Sep-19

Awesome course. Could be improved by having more guidance on homework

By Keisuke F on 14-Sep-19

I had a very good time with linear algebra.

By Anthony S on 14-Sep-19

Great course and taught me insight into why linear algebra is useful!

By Pinas G on 14-Sep-19

Excellent course!! The Mathematics for Machine Leaning : Linear Algebra offered by the Imperial College of London it's a good step into building a strong foundation in the field of Linear Algebra.

By Md. Y H on 12-Sep-19

It gives a great visual intuition on linear algebra topics.

By Gabriel L on 10-Sep-19

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.

By Raivis J on 9-Sep-19