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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
10,863개의 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

CS

2018년 3월 31일

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.

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.

필터링 기준:

Mathematics for Machine Learning: Linear Algebra의 2,180개 리뷰 중 2001~2025

교육 기관: praneel a

2021년 7월 7일

very nice

교육 기관: Zala R

2020년 5월 26일

fantastic

교육 기관: KRAKOU D G S

2020년 5월 23일

very good

교육 기관: Sharob S

2019년 3월 4일

Loved it.

교육 기관: EL O A

2018년 5월 20일

Very nice

교육 기관: NITESH J

2020년 7월 5일

TOO long

교육 기관: TAVVA G M

2020년 5월 17일

good one

교육 기관: Thita A I S

2021년 3월 2일

thank's

교육 기관: Millati A L

2021년 3월 25일

yesss

교육 기관: G A N M

2020년 10월 14일

Good!

교육 기관: Deleted A

2018년 9월 27일

Good!

교육 기관: venkatadurga P

2021년 9월 13일

good

교육 기관: Persis

2020년 7월 18일

gfhf

교육 기관: Zhassulan S

2020년 5월 24일

Good

교육 기관: Ishan Y A

2020년 5월 19일

nice

교육 기관: Li J

2018년 5월 20일

nice

교육 기관: Reed R

2018년 7월 14일

The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e. (a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools. While I appreciate this goal and enjoyed the early foray into projection, I never felt the "a ha" moments I did as an undergrad in a class that used Gil Strang's "Introduction to Linear Algebra" (which I reread alongside this course as a supplement). The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course. It could be that I was just looking for a more complete treatment of the subject than the course ever intends to offer, but I strongly felt that with a bit of restructuring, that the subject could be presented primarily intuitively, but with a level of clarity and artfulness in its conclusion that will ensure that students remember the core concepts beyond when they remember its presentation.

교육 기관: Eitan A

2020년 1월 12일

As of this writing, I am almost done with week 4 of Mathematics for Machine Learning: Linear Algebra. The content of the course is excellent and professor David Dye's lectures are to be commended no doubt. The reason for my low rating is because the programming assignments are broken and that's really not acceptable for paid offering such as this. To clarify, at various points throughout this course, students are asked to complete a programming assignment. The student is presented with a button which says, "Open Notebook". The student is supposed to click this button and be redirected to a Jupyter Notebook (and interactive Python execution environment). Unfortunately, instead of being redirected, click on this button results in a "404 Not Found" error. There are various discussions in the class discussion forum regarding this issue (some months old), but no action has been taken to resolve this issue. Luckily, someone taking the course managed to find the programming assignments and posted them on google docs for others to use. I've been working these which is fine, but as I said, we're paying for these courses, someone should be resolving this.

교육 기관: Maprang

2020년 6월 16일

I never took Linear Algebra in university. The last time I got exposed to this topic was more than 10 years ago when I was still in junior high. This course is very condensed. Each video covers each topic relevant to ML very briefly and the instructors go very fast on explaining each topic. This means students have to do a lot more research on their own to really comprehend the concepts. What's nice about this course is the programming assignments. They give you a chance to apply math concepts to the computational model. Something like this you wouldn't have a chance to do if you don't spend on an online course like this one, I guess. Overall, I think this course provides values in a way that gives you an overview of how Linear Algebra is used in ML. For me personally, I know I still need to consult other sources online to further understand Linear Algebra as I'm not sure that after finishing this course I've got adequate knowledge to pursue ML. What all that said, hence I give this course 3 stars.

교육 기관: Khai T

2022년 2월 20일

I personally do not recommend this course for anyone who has not had any prior knowledge in linear algebra. Although it states that this course is for beginner, you should have a (quite) decent background in linear algebra to understand all the materials. The geometric intuition parts of projection and eigenproblem are interesting. I would suggest the instructors to prepare some lecture notes for each week, instead of giving just a single cheatsheet at the end of the course. This would help learners to review the material better. Besides, some videos do not have the subtitle synced. Thus, matching between the full transcript and the video is quite hard for non-English speaker.

교육 기관: Avinaash S

2020년 9월 9일

The lecture material in this course is great, and the quizzes are a lot of fun and it provides good resources for learning. However, the programming assignments are a pain due to lack of guidance and the grades are penalized due to minor things like indentations as opposed to actual math errors. This isn't a python course, its a math course, and grades should be awarded and penalized based on the math skills one has acquired throughout the course, not on the programming or whether an indentation is off. I highly recommend the course to learn linear algebra but I strongly encourage the instructors to improve the programming assignments or alter the assessment methods.

교육 기관: MR T

2020년 4월 24일

It must be difficult to pitch the level of these courses.

I have been taught Data Science whilst on an apprenticeship but didn't feel the maths was taught rigorously enough and hoped this would fill gaps of in knowledge.

The breadth of the concepts covered on the course achieved that but a lot of research was required from other resources to clarify certain topics which is why I think a beginner rating for this course might not be fair.

If you are not confident with maths, this course is achievable but expect to devote time to on other sites.

The PDF supplement is concise but useful for reference

교육 기관: Anne R

2022년 1월 18일

T​his course was helpful in reviewing topics in linear algebra and focusing on the usefullness of projections and eigen problems in manipulating vectors. If you have taken a linear algebra course previously you should find the course pretty easy. If you have not taken a linear algebra course before, this course may be much more difficult to complete unless you are prepared to use other resources to help manage the course topics and assignments. I was hoping for more challenging course assignments but understand that much of it is self-assessing what you are learning as you go along.

교육 기관: Constantin N

2022년 8월 1일

The topic is presented really interestingly and encouraging, but then it becomes very cluttered or very abstract. The worst part is that after some 5 minute video about one concept you're supposed to answer a quiz with calculations you haven't even gotten the formula or an example. Those problems are mentioned several times in the dicussion board. It's a shame since the teacher sincerely likes this topic but fails to convey it in a illuminating manner.

If you're up to look up other examples after every lesson, this course is okay, but it's not worth the 50 € subscription.

교육 기관: Meng Y

2020년 7월 26일

Sometime the course does not clarify some principle. Also, I still cannot understanding that why the eigenvectors have relationships with page rank and why can we use the probability of reaching the link to each page as a vector. I cannot understand the relationship. Plus, the final quiz contains something that I have not learnt in the course, such as damping. I still cannot understand the Quiz2-5. I learn much in courses week 1-4, but I am much confused about the week 5. Thank you for listening.