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.
автор: Xin Y•
Apr 09, 2020
This is an excellent course as a refresher of the basic concepts in my college linear algebra. The instructors really put a lot of effort into making all the course materials. I enjoy the animations a lot! I am not a pro in Pandas but the programming assignments are actually very well-explained and perhaps a bit too easy. I'd thought they would put some plots and twists in the programming assignments. Very helpful course and great instructors. Thank you!
автор: Maximiliano B•
May 24, 2020
This course is excellent and it provided me a very good refresh about the linear algebra theory that I’ve learned in my graduate studies. The professor are great, the videos have an appropriate duration, and they help you build the intuition incrementally every week. The Python assignments are relative easy but they are of great value. I definitely recommend this course and I am looking forward to start the next course of the specialization.
автор: Orlando F•
May 24, 2020
A comprehensive course in Mathematics and Linear Algebra. If you're not related, or with rusted maths, don't be afraid, it will work for you, but it will demand some amount of time. A good time of course. Here I learned things I didn't fully understand. Great teachers. Some misses on explanations, will push you to Khan, tutorials, or books. Recommended course for everyone interested in getting in ML, AI, DS. A great introductory course.
автор: Harsh D•
May 03, 2020
Certainly the best online courseware I have attended. Prof. Dye breaks down most typical concepts of mathematics in simple and easy to understand blocks that makes this course fit for anyone. He brings out an interesting dimension to every concept that makes you comprehend it well and you're equipped to understand the practical applications of it. Would recommend to anyone looking brush their concepts of linear algebra.
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.
автор: Srutimala D•
May 12, 2020
The connection between machine learning ad vectors got clearer as the course moved ahead. The quizes are detailed and requires actual understanding of the concept which is not hard to grasp once you pay attention to the lecturers who themselves are so passionate about the subject, makes me excited to learn too. I can say, I finally, after leaving high school, have understood high school maths and it's applications.
автор: 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.
автор: Rajakrishnan S•
May 29, 2020
awesome content with excellent pace. no bullshit during lectures. only place for improvement would be to give relevant content in readings as the course feels just of videos and less reading materials for reference. Ofcourse ,one can look up in textbooks , but giving the reading materials in the course will improve the readability and findability and will be according to the lecture content. thanks for asking!
автор: 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.
автор: 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).
автор: Digeesh J•
May 21, 2020
This course is good for everyone, as rather than diving deep in the paper pen model, the intuition is taught. For those who already know Linear Algebra, it is best to take this course to understand, what you are writing and what each formulae is doing. For those who dont know anything about Linear Algebra, but is interested in Machine Learning, do this course to atleast have the intuition behind it.
автор: Dr. S M S•
May 01, 2020
I am very grateful to the in structures and the platform providers who designed the the course to enrich the knowledge of mathematics in good manor. From this course I learned a lot. Being a mathematician I feel that there is a need to change form traditional teaching to technological oriented teaching. This course helped me in finding such a path.
Dr.Shaik Mohiddin Shaw
автор: Edisson O A C•
Apr 29, 2020
Great course as a starter to understand the basis of linear algebra in machine learning. I had already taken a course of linear algebra as an undergrad but this course really opened my view of the applications and importance of some concepts I understood in a merely abstract way. The instructors are not only excellent in their explanations but you can also feel their interest for the subject.
автор: 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.
автор: 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.
автор: 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.
автор: Stefan R•
Apr 28, 2020
It is usefull and good course. It does require at least some familiarity with the concept or otherwise you will spend hours trying to understand how things happen, but lecturers have overall given great insights in how things work and tried to simpliify as much as possible.
Maybe would be usefull for course creator to add some optional basics tests on the topic, for total beginers.
автор: 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!
автор: 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.
автор: Badri A•
May 01, 2020
At first, I was kinda of afraid of Math in general and Linear algebra in particular, but after taking this course, I am satisfied with it.
A special thanks to the instructors and all the people behind this course, for making thing simple and comprehensible, and at the same time, hit the target. Looking forward to keep learning and carry on with this specialization !
автор: Rhea B•
May 20, 2020
This course was very informative . Having learnt to solve most of this problems by hand in under-graduation , this course helped me to code these hand-worked problems . Additionally I was able to understand and visualize what the problems actually do . I highly recommend this course for anyone who is looking to learn or advance their career in machine learning .
автор: 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.
автор: Sridhanajayan S•
May 31, 2020
This is an exceptional course for learning Linear Algebra in an intuitive way. i would recommend this course to everyone who is fond of mathematics. This course will also have programming assignments with python and numpy packages. Overall I had a wonderful experience and a handful of knowledge. Thank you for the course creators and professors and lecturers.