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.
Sep 10, 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.
創建者 Peter B H•
Nov 27, 2019
The content was good, but a couple of times what was said didn't gel with what was being drawn/written/done. Since I'm learning, this took me longer to double check when I misunderstood something whether it was the concept or a mistake in the delivery.
創建者 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.
創建者 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
創建者 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.
創建者 Maximilian P•
Dec 12, 2018
Some exercises are completely incoherent to the preceding videos, which makes it very difficult to solve them. very frustrating
創建者 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.
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.
創建者 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.
創建者 Arno D•
Dec 19, 2018
Some concepts were not clearly explained and there were a lot of issues with assignment grading working properly.
創建者 PRAKHAR K•
Mar 11, 2018
Not good, concepts not explained clearly.
創建者 Richard C•
Oct 16, 2018
Does not explain mathematics in videos
創建者 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.
創建者 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.
創建者 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.
Mar 22, 2018
This is such a great course for student already have background about college level linear algebra knowledge, but don't know the under relationship among those terminologies. For instance, after this course I finally know what is dot product means, what is eigen characteristics. The content of this course are well prepared, this is such a masterpiece from Imperial College London. Thanks to all stuff behind this course.
創建者 Ashish D S•
Apr 09, 2018
This is excellent course on Linear Algebra. The best part of this course is, lectures focus on the physical interpretation of the topics rather than making you practice formulae without understanding. This course helped me refresh my Linear Algebra concepts and also helped me better understand change of basis and Eigen related concepts.
Many many thanks to professors for excellent course design and presentation.
創建者 Vincent L•
Jun 09, 2018
I took this course as a review for my data science curriculum. Previously, I was having trouble recalling the details of matrix arithmetic which was making it hard for me to get a deeper understanding of machine learning. After doing this course, you should have no trouble following along. For those already familiar with the material, it should take about 1-2 weeks to complete if working at a leisurely pace.
創建者 Joseph F•
Jun 20, 2019
This course is perfect for many including those, like myself, who haven't seen this for 20+ years. I can imagine that it would be helpful to have, at least, a proclivity towards programming if you do not have familiarity with a programming language (at least course comments tend to reflect this).
For those experienced with coding, no difficulty will be encountered, as focus here is trivial (numpy libs).
創建者 Lisa M•
Apr 07, 2018
This was a fantastic course. I'm new to linear algebra, so it was bit intimidating even signing up (!) - but the lecturers were really, really good about explaining all concepts from the ground up so it was always possible to visualize and extrapolate from solid foundations. For me it was a stretch each week, but in a good way: very challenging, but achievable with enough planning and effort.
創建者 Ying T•
Mar 09, 2018
An awesome course with high quality video lectures!! I will recommend this course to anyone who's looking for a refresher or quick pick-up on linear algebra. The homework's compatible with the materials and is quite interesting. The lecturer also did a good job on explaining critical concepts with easy but good examples. I'm looking forward to more similar courses from Imperial College.
創建者 Jayant V•
Mar 29, 2018
I have taken a course on linear algebra during my graduate program and must admit that it was not one of my more comfortable ones! Coming back to this course online, it really did help me get a much better understanding of concepts like dimensionality, basis, eigen values and eigen vectors. I intend to go over the lectures at least a few more times to be sure I have understood it well.
創建者 Huy M•
Mar 11, 2019
I've only done half of the course but I already know this course is one of the best on Coursera! Complex concepts in mathematics are broken down into simple terms. The professor also clearly stated what those concepts are used for in practical, which certainly help learners have a clear idea of why they are learning this course. Very exciting every time I click onto new lessons!
創建者 Ramon M T•
Aug 20, 2019
Excellent Course, I remembered the linear algebra that I saw in school more than 26 years ago (I studied applied mathematics and switched to Actuaria), but now with examples related to DataScience.
For someone who has not programmed in some language the exercises can be challenging, but they are always very intuitive if the example steps are performed.
創建者 Art P•
Jun 08, 2018
This course was of high quality, was very helpful in explaining some key concepts and I appreciated the instructors energy and humor. My only complaint about the course is that some of the quizzes and homework assignments felt significantly more challenging than what was covered in the lessons; however, the discussion forums proved helpful in closing this gap.