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Mathematics for Machine Learning: Linear Algebra(으)로 돌아가기

임페리얼 칼리지 런던의 Mathematics for Machine Learning: Linear Algebra 학습자 리뷰 및 피드백

4.7
별점
10,879개의 평가

강좌 소개

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....

최상위 리뷰

NS

2018년 12월 22일

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.

PL

2018년 8월 25일

Great way to learn about applied Linear Algebra. Should be fairly easy if you have any background with linear algebra, but looks at concepts through the scope of geometric application, which is fresh.

필터링 기준:

Mathematics for Machine Learning: Linear Algebra의 2,181개 리뷰 중 2126~2150

교육 기관: Dave D

2020년 4월 20일

Quizes and practice material were either overly repetitive or were not supported by the materials. If the course is based on exercises, then a bit of time should be spent on review of the solutions and methodologies for solving. The forum is fairly disorganized so did not solve this purpose.

교육 기관: Steven M

2021년 2월 14일

I did not enjoy this course. The videos were good but covered the material very quickly and did not leave you well prepared for the assessments. I found the assessments impossible without a lot of extra study using other materials - if this is intentional it should be made clear.

교육 기관: Tama H

2020년 7월 18일

1) There are not enough examples on some subjects 2) The instructors explained the theories and intuitive behind it very well, but the exercises are sometimes too difficult 3) Coding exercises are very challenging and I'm not sure what good it could bring to my data science journey

교육 기관: Hayden R

2020년 9월 3일

This course does a poor job of teaching Linear Algebra, it skims through the topics and gives pretty difficult coding work with poor instruction. This course should only serve as a refresher, do not expect to learn much past the first 3 weeks.

교육 기관: Niklas Z

2021년 8월 19일

It covers the basics, but frequently does not explain how it arrives at definitions or some steps in derivations. I spent a lot of time filling these gaps for myself using other (free) resources such as math stack exchange or Khan academy.

교육 기관: Patrick W

2021년 5월 1일

Not an intro course. Probably a better review course. I spent a lot of time on other web sites to get more background info to be able to do the problem sets. On the plus side, the instructors are very enthusiastic.

교육 기관: Glenn

2021년 5월 13일

This course is not suitable for beginners. Topics are touch-and-go, and many LA concepts are assumed of the learner. I had to basically learn the content on YouTube and then come back to complete the assignments.

교육 기관: Manoj B

2020년 9월 12일

Not a great course. It started alright but really it lost track after two weeks. No references were made to the actual ML applications. This was more like a pointless Matrix knowledge.

교육 기관: Gurrapu N

2020년 4월 5일

There is hardly any relation between videos and assignments. There should be a way to let the students know the right answers after completing the course to rectify the mistakes.

교육 기관: Shrinath I

2020년 4월 12일

It was a decent course, but the teachers weren't particularly good. I had to often rely on other resources to figure out what was going on. That kind of ruined it for me.

교육 기관: Muhammad Z A

2020년 6월 9일

Not a Good Course for Biggner's. Instructor do not have ability to simply topics taught in this course. They were just copy pasting its materiel from other slides.

교육 기관: kumar s

2020년 9월 7일

Instructor Taught good at the beginning till week 3, but not good after that. very disappointed. Will Never recommend any of my friend or classmate this course.

교육 기관: ASWINI R

2020년 9월 11일

i have not learned python so i am unable to do the assignments... the classes are too good to miss a certificate, but only if i could manage the assignment...

교육 기관: Mathew S

2021년 6월 23일

Forums are a bit dead. Most helpful (and frequent) answers are a link to khan academy videos explaining the same thing, but better.

교육 기관: Justina N

2020년 8월 6일

The final module went too fast and skipped over a lot of explanation. this really does under the good parts up to week four.

교육 기관: Kevin H

2022년 2월 12일

I had to go outside this course to get and understanding of what they were talking about. This could greatly be imporved!

교육 기관: Graham G

2020년 3월 26일

Not nearly enough detail on the concepts. I had to watch all of Khan Academy's Linear Algebra course to understand.

교육 기관: Boyang H

2022년 6월 13일

I can get everything Quizzes and Tests right but I have no fucking idea what I am doing.

교육 기관: Roy M K

2021년 2월 3일

There are few or no worked out examples, making it hard to solve quizes.

교육 기관: Tri V Q

2022년 7월 24일

Many parts are too obscure such as the Page Rank part.

교육 기관: Achal A

2021년 8월 23일

more reading content is needed

교육 기관: Rishabh J

2019년 2월 27일

Not as challenging. Too basic

교육 기관: Andrey L

2021년 3월 23일

Not for the beginner at all

교육 기관: Yash G

2019년 12월 14일

not clearly explained

교육 기관: Lee B

2019년 7월 10일

it is too expensive