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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

10,706개의 평가
2,130개의 리뷰

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


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.


2019년 9월 9일

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.

필터링 기준:

Mathematics for Machine Learning: Linear Algebra의 2,141개 리뷰 중 1576~1600

교육 기관: Merey O

2020년 12월 11일


교육 기관: Yiya W

2020년 11월 30일



2020년 7월 25일


교육 기관: Chow K M

2020년 7월 24일





교육 기관: Oo K T

2020년 7월 22일


교육 기관: V K M

2020년 7월 17일


교육 기관: Akshatha Y

2020년 7월 16일


교육 기관: Bhoomika H

2020년 7월 15일


교육 기관: BALAJI.V

2020년 7월 13일


교육 기관: RAMÍREZ S C A

2020년 6월 29일


교육 기관: Nalongsone D

2020년 6월 16일


교육 기관: Vinish R

2020년 5월 12일


교육 기관: eli z

2020년 4월 12일


교육 기관: Akhil18 V

2019년 7월 31일


교육 기관: Salem A A

2019년 7월 1일


교육 기관: 宋健

2019년 3월 12일


교육 기관: Yiqing W

2018년 12월 27일


교육 기관: MD K A

2020년 7월 14일


교육 기관: Daniel R

2018년 8월 12일


교육 기관: Deepak K A

2018년 6월 19일


교육 기관: Joseph S

2021년 9월 27일

교육 기관: Rayanne

2019년 10월 21일


교육 기관: Tushar S

2019년 3월 27일


교육 기관: John F

2020년 4월 23일

It's a good overview. I think that to get a lot out of this course it would help to have at least encountered basic matrices, vectors etc before. It's not that these concepts aren't introduced it's just that I can imagine if you have never encountered these things before you might get overwhelmed a bit quickly. It would also help if you have some rudimentary knowledge of programming i.e. know basic syntax, what a for loop or a while loop is and other basics. I know a bit of programming and i'm pretty ok at math so the course was manageable for me. Especially good was showing how all of the concepts learnt can be applied to understanding the Google Page Rank algorithm.

The best part of this course is the conceptual overview it gives and the instructors constantly reiterate how this type of understanding is more important than just being able to chug through a whole lot of algebra. Computational skills aren't really that important because apart from very basic examples, a computer is pretty much necessary to do the calculations anyway and as we all know, just because you know how to plug stuff into a formula doesn't mean you have the faintest idea what you are actually doing!

I think a very bright person could probably fully understand this course coming at it from scratch but I know that I would have struggled if i'd never glanced at the math or done some basic programming before.

교육 기관: Vern

2018년 4월 10일

I would give this course 5 stars for the fact that in five weeks, the course is able to go through perhaps a semester or two or three of Linear Algebra (LA), and how LA fits into data science. I gave it four stars because I believe the program should include lots of links to reference and learning aid resources. Because I had done a couple other courses on LA relatively recently, some these arcane LA concepts were grasped with some, but not too much, effort.

If you are even just a little familiar with LA, this course will give you a good foundation for the LA relative to data science. So, if this is you, and you want to get into Machine Learning (ML) to understand how ML works internally, then jump right in.

Thanks to all who contributed to make this a great ride.