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Вернуться к Mathematics for Machine Learning: Linear Algebra

Отзывы учащихся о курсе Mathematics for Machine Learning: Linear Algebra от партнера Имперский колледж Лондона

4.7
звезд
Оценки: 5,054
Рецензии: 931

О курсе

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....

Лучшие рецензии

EC

Sep 10, 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.

CS

Apr 01, 2018

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.

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851–875 из 927 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: Andres O

May 25, 2018

Very good linear algebra intro/refresher

автор: danthedoubleD

Feb 23, 2019

good stuff hopefully, i will be useful

автор: Earneet

Jul 08, 2019

i don't know , just finish it so soon

автор: Aditya R

Jul 22, 2018

Really it was a very nice course .

автор: SIMONOT M

Mar 29, 2019

Clear and interesting. Thank you.

автор: Idriss M

Sep 23, 2019

great intro to linear algebra

автор: Yijie X

Aug 14, 2018

greate course for beginners!

автор: Rishabh C

Mar 01, 2020

Nice Content and lectures.

автор: rasheeq i

Apr 19, 2019

Should go more in details.

автор: Enzo M

Feb 26, 2020

quite complex but useful

автор: 田德宇

Jun 23, 2019

no lectures, only videos

автор: AB

Mar 03, 2019

Great to relax and learn

автор: Vaibhav S

Sep 10, 2019

can be more detailed

автор: Rohit V

Nov 19, 2019

thanks coursera....

автор: Badal S

Feb 10, 2020

Good Understanding

автор: Hemant D K

Dec 16, 2018

Very Informative.

автор: snehashis p

Jan 23, 2019

very good course

автор: Siddharth S

Oct 16, 2018

A nice course.

автор: Akhil K

Oct 21, 2018

great course

автор: Sharob S

Mar 04, 2019

Loved it.

автор: EL O A

May 20, 2018

Very nice

автор: Luciano M

Sep 27, 2018

Good!

автор: Li J

May 20, 2018

nice

автор: Reed R

Jul 14, 2018

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.

автор: Eitan A

Jan 13, 2020

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.