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.
KK
Apr 4, 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.
2076 - Mathematics for Machine Learning: Linear Algebra 的 2100 个评论(共 2,485 个)
创建者 Dhruv A
•Jul 31, 2020
Brilliantly conducted. Provides a great introduction to linear algebra allowing the learner to start diving in further.
创建者 Daniel T
•Jun 18, 2019
It would be better if it had lecture notes. Reviewing the material and writing it down requires rewatching the lectures
创建者 Robert S
•Aug 18, 2018
The linear algebra was taught in an easy to understand manor but the applications in machine learning were quite sparse
创建者 Marcos G A
•Jul 7, 2020
Should share additional videos (like links to khan academy if the student needs to learn more or wants to get deeper).
创建者 Weiyu G
•Aug 12, 2019
It is really intuitive and good for people who have little idea of Linear Algebra. The best part is the PageRank Algo.
创建者 James H
•May 24, 2018
Programming assignments failed to save and submit sometimes. But the course itself was well taught and at a good pace
创建者 Alex S
•Aug 7, 2018
The instructors are very good in delivering the course content..However, more reading resources should be provided.
创建者 Isaac N
•Apr 18, 2018
Provides a good understanding of Linear Algebra for Machine Learning. However, it is a little lacking in exercises.
创建者 Rishabh D
•Mar 15, 2020
There were some problems with the notebooks used in the assignments but apart from that this course was brilliant.
创建者 SAMUEL D S A
•May 9, 2020
Achei que haveria uma pequena introdução ou explicação de como seria usado esse conhecimento em Machine Learning.
创建者 刘静怡
•Jan 12, 2019
Nice course! Hope to improve the programming function. It is really hard to find the errors in it for a greenhand
创建者 Matheus d A
•Aug 8, 2023
The two first courses are excellent, the final course is full of bugs and errors and a general pain in the butt.
创建者 Douglas C
•Jul 2, 2020
Good course. It will be great if this can also include material if you want to go deeper after taking the class.
创建者 Alex G
•Jun 14, 2020
Went a bit too quickly for me towards the end of the course (coming into this as a layman), otherwise very good.
创建者 Jordin W
•Jan 14, 2020
Fantastic course. My favourite delivery of Linear Algebra thus far. Both Sam and David were a joy to learn from.
创建者 Umang S
•Dec 21, 2019
The course is very comprehensive and yet is very focused towards actual application of LA in Data Science and ML
创建者 Walter S
•Feb 9, 2021
Very good course overall. I would have liked more explanation on the exercises and more time working on python.
创建者 Varun T
•Nov 6, 2022
A good quick introduction for people who already know the concepts but need refresher to get back on the feet.
创建者 Ahmad H Z
•May 21, 2022
Gives you an overview of how linear algebra can be applied to solve real world problems. I liked it very much.
创建者 Saurabh G
•Nov 6, 2019
This is one of the most important courses for someone who wants to build career in the machine learning field.
创建者 Shriniwas S U
•May 10, 2020
Good course but Instructor should provide some lectures on python programing which is related to assignments.
创建者 Rodney N d S
•Aug 31, 2018
This course is short, you can conclude it in a month, but you will learn a lot with the assignments in Python
创建者 tao t
•Mar 23, 2022
love it. However, it went a bit fast and I wish the parts of eigen stuffs had been slower and more detailed.
创建者 Chakola P J
•Aug 23, 2020
The course provided a good insight into some of the essential concepts with respect to vectors and matrices!
创建者 Ronast S
•Jan 18, 2020
This course provides basic insights about vectors and matrices and their analysis in multidimensional space.