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Learner Reviews & Feedback for Mathematics for Machine Learning: Linear Algebra by Imperial College London

4.7
3,716 ratings
670 reviews

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

NS

Dec 23, 2018

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.

CS

Apr 01, 2018

Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.

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26 - 50 of 670 Reviews for Mathematics for Machine Learning: Linear Algebra

By khaled W S

Mar 25, 2019

totally enjoyed it. requires a bit of side research as any online course would. some of the quizzes were not directly related to the video that preceded them as one would expect. However, a fun course and covers a lot of important basics for it's relatively short duration.

By Philip A

May 16, 2019

Excellent Instruction

By JUNXIANG Z

May 17, 2019

This course reviews the essential concept of linear algebra in the context of machine learning. However, it would be much better if it provided more optional exercise and reading materials.

By Ralph T

May 04, 2019

decent course. It gives a good enough background to understand the mathematics necessities of many areas of data science. could be more thorough and dive deeper into some of the content.

By Mark J T

Aug 02, 2019

Good course because it shows how to understand geometrically, things that I had hitherto only understood computationally.

By kai k

May 05, 2019

many of the activities are excellent, but videos hard to follow along to at times - play them at 0.75 speed if you can. Also, the faculty is not super responsive it seems on discussion boards creating some confusion

By Maximilian P

Dec 12, 2018

Some exercises are completely incoherent to the preceding videos, which makes it very difficult to solve them. very frustrating

By Girisha D D S

Aug 27, 2018

Although the course content is good, I feel it could have been done better. I enjoyed the multivariate calculus course compared to this course.

By Pedro C O R

Aug 02, 2019

The topics could be improved in the way they are presented. I always had to search for additional material.

However, the course is okay, it could be better, the forum is not that active, and some assignments are good.

By Jorge N G

May 02, 2018

Mainly explains how to operate with matrices and vectors. Not how to use those in machine learning. If you expect to have a clear view of the usefulness of eigenvectors and eigenvalues in machine learning, this is not your course.

By Mesum R H

Aug 26, 2018

The course tries to cover every edge of Linear Algebra but fails to integrate each step with what relationship it has with Machine Learning. Core Formulas and Mathematical derivations are shoved down from throat without any respect for learners from non-engineering or computer science background. Other than week 1,2 rest was completely case study or example less UN-intuitive lectures of matrix formations and transformations. Needs a severe revamp with better examples and broader picture.

By Richard C

Oct 16, 2018

Does not explain mathematics in videos

By Arno D

Dec 19, 2018

Some concepts were not clearly explained and there were a lot of issues with assignment grading working properly.

By Anonymous

May 09, 2018

The content and the speed are not satisfactory.

The speed totally hampers the content, lots of things aren't explained especially after Sam took over in the last module.

Other than the first 2-3 intuition videos and the programming assignment nothing was good in the 5th module/week.

It was very very difficult to follow the page rank video. I still don't understand it. For eigen basis I had to refer to other material outside this course.

By PRAKHAR K

Mar 11, 2018

Not good, concepts not explained clearly.

By Dmitry R

Jan 13, 2019

Authors try to teach babies. Might be good, it is hard to judge for me as I know linear algebra. Definitely boring to me. For example 3Blue1Brown (which they reference btw) is ingenious in my opinion, so it might be not me who is the problem.

But the quizzes just don't make sense! The ones where solving problems involved might have 2 numerically right answers but only one of two is treated as the right. And there are just idiotic or not covered in lectures answers for quizzes without problems.

By sitsawek s

Sep 14, 2018

Quite difficult for learner who didn't know about linear algebra.It jump and few example and skip a lot of part for understand.But good for recall.

By Patrick B J

Jul 25, 2018

Hands down the worst course I've ever taken in my life! Poorly put together and extremely short videos that don't provide an adequate amount of knowledge especially in relationship to the given quizzes. I truly hope this course is removed.

By Jinwoong K

Nov 24, 2018

nice intro to the linear algebra world and relevant code

By Serge H k

Nov 25, 2018

I love the stuff that I learned: the usefulness of eigenvalues and eigenvectors, coding pagerank algorithm, gram Schmidt to create orthonormal basis, ...

By Wenyuan Z

Dec 14, 2018

good for people who want to understand linear algebra from a geometry aspect

By Dong T

Dec 15, 2018

Very practical and dynamic tutorial of linear algebra. thx.

By Boyan S

Dec 15, 2018

Excellent Course.

By David H

Dec 17, 2018

Well designed for students who want to understand Linear Algebra for ML.

By imran s

Dec 17, 2018

Good Explanation and Useful