学生对 Imperial College London 提供的 Mathematics for Machine Learning: Linear Algebra 的评价和反馈
课程概述
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GB
Aug 16, 2020
The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.
EC
Sep 9, 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.
401 - Mathematics for Machine Learning: Linear Algebra 的 425 个评论(共 2,462 个)
创建者 Mufassir A
•Jan 12, 2025
Clear and Understandable Explanations. This course inspired me on exploring the applications of linear algebra other than machine learning. I really liked the explanations. Thanks for this amazing course
创建者 Antareep D
•Oct 5, 2022
Exceptional! Very concise theory , great real life examples , practical importance and very good programmning quizzes, really enjoyed learning and thanks to coursera for making this course accessible !
创建者 Saket S
•Mar 23, 2019
This course was very concise and to the point as far as the field of linear algebra in machine learning is concerned.I learned a lot and will also like to take up the next course of this specialization.
创建者 zahri h
•Mar 6, 2021
little bit hard because the language is english and the math word in englist to make me confused and dizzy. I hope this course available Indonesia language for the next students who wants to learn this.
创建者 Tim L
•May 27, 2019
Thank you for providing this course. An advice would be to have the programming assignments start out with more guidance before letting the student do it. The Page Rank assignment for example was great.
创建者 Yutong Z
•Apr 16, 2019
So great in general! But since it is not a pure maths course, some concepts are not explained in depth. It's a perfect course for self-learner because you can always go to the forum to look for answers.
创建者 Michael A P
•Sep 9, 2023
Great class, you'll learn a lot. Class is listed as Beginner, but it I think it is much harder than "Beginner". I took Linera Alg ~35 years ago and thought this would be a review. Worth the time |:-)
创建者 Hanadi S
•Jun 11, 2023
I appreciate the way that you taught this course. I loved your joy of teaching and making sense of linear algebra in machine learning, and looking forward to joining the next course.
Thank you so much!
创建者 Juan P M C
•Aug 7, 2020
Excellent course, perfect for anyone who wants to get started in Linear Algebra. The teachers explain everything clearly and the assignments are of the proper difficulty in order for you to understand.
创建者 T K
•Jul 18, 2020
A well packed non conventional course on linear algebra, full of interesting geometric interpretations, difficulty of the course is absolutely perfect, i am going to recommend this course to my friends
创建者 Om K
•Mar 22, 2020
The course is good because it doesn't just teach all of linear algebra like some other courses. It only teaches what is specific to Machine Learning which actually saves a lot more time than you think.
创建者 Geoffrey B
•Aug 17, 2020
The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.
创建者 Mahyar G
•May 31, 2020
A great course covering key aspects of Linear Algebra with the lecturers giving a good intuition about what's going on with the subjects.
(PS. I loved the sense of humor of Prof.Dye it was really fun!)
创建者 Shuang J
•Nov 17, 2019
This course is not for people without any knowledge on Linear Algebra, but for those with who want to build intuitive understanding of how Linear Algebra works.
I particularly like Dave's instruction.
创建者 Vitória C
•Jul 11, 2018
The content of the course is very relevant, and the instructors are really fun and helpful.My only suggestion is to upload revisions for each assessment, so we can understand what we are doing wrong.
创建者 Lukasz K
•Oct 27, 2023
Very good course. I liked very much the way the topics were presented and explained. I especially appreciate David Dye's clarity of explanations, enthusiasm, passion, and joyful attitude. Thank you.
创建者 Bincheng W
•Mar 4, 2019
Satisfactory. Most satisfactory. Actually, this course is possibly the best linear algebra MOOC class in terms of instructor teaching style and how they pick and convey the most insightful concepts.
创建者 David V
•Jun 25, 2019
This was a terrific course; the instructors' are passionate and knowledgeable about the course material, the assignments are engaging and relevant, and the length of the videos feels "just right".
创建者 Pinas G
•Sep 14, 2019
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.
创建者 Jitender S V
•Jun 29, 2018
This is the BEST course if anyone wants to learn linear algebra for machine learning. Lectures are clear and very understandable and quiz questions are great, too. Thank you for this great course.
创建者 Dinesh T
•May 30, 2021
This is a great course to built foundation for Machine Learning. Both the lecturers are amazing and great use of technology in presenting the concepts. Great example linked to PageRank algorithm.
创建者 Anurag S
•Jul 11, 2019
It's a nice course but instructors should go in more details. It's mostly high school mathematics. I was expecting undergraduate level Linear Algebra. Otherwise it was a good learning experience.
创建者 Peter H S
•May 9, 2018
Excellent course on the relevant parts of linear algebra for CS. Both instructors are great fun to watch and the assignments use up-to-date Python programming and Jupyter notebooks. Well done !!!
创建者 James B
•Jul 31, 2020
Efficient, targeted course for learning the language and basic operations within linear algebra. Excellent for those working full-time, and for those without much experience with linear algebra.
创建者 kmccall
•Apr 5, 2020
really good. i would have been fine with a slightly longer course that worked through more examples and alternative explanations in order to ensure more solid understanding of complex concepts.