Вернуться к Mathematics for Machine Learning: Linear Algebra

4.6

Оценки: 2,619

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Рецензии: 461

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....

автор: NS

•Dec 23, 2018

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.

автор: PL

•Aug 26, 2018

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.

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Рецензии: 457

автор: Nelson Fleig Aponte

•Apr 25, 2019

This is a great course! Be advised: It is very challenging and will kick your butt if you haven't seen much linear algebra before. The content in the course won't always be enough to solve all of the assignments. But look into the forums and use some other sources and you will succeed in this course. Overall I am glad I took it even if it will take a little longer until I can say that I master everything that was covered in the course.

автор: Manish Gupta

•Apr 23, 2019

Really good course. Nice instructors.

автор: Pirkka Penttinen

•Apr 23, 2019

Too many sessions and quizzes which appear to require previous knowledge of the taught subject, concept and the details. If I had that knowledge already, I would not be taking the course to begin with. The programming assignment do require previous Python/other programming experience. I would not categorize this as a 'beginner' class.

автор: Tobias Kahan

•Apr 22, 2019

Great review of a topic I learned in College. Not sure how it would be for the first time, would probably take more repetition on certain subjects. Maybe going over the videos multiple times.

автор: Tirthankar Banerjee

•Apr 20, 2019

Excellent intro to Linear Algebra with clarity on concepts such as application of Gram Schmidt method and Eigenvectors.

автор: Marc Pfander

•Apr 19, 2019

Excellent course to refresh linear algebra basics, build intuition and see the subject from a machine learning perspective. I wouldn't recommend it for people that are new to the subject, since the pace is fast, much is omitted and the assignments aren't always easy. Every now and then, the calculations come before the intuition, which can be tricky to follow. However, most of the course is very didactic and the combination of videos and challenges kept me motivated throughout.

I suggest the youtube channel of 3Blue1Brown whenever you feel lost with the subject at hand.

автор: rasheeq ishmam

•Apr 19, 2019

Should go more in details.

автор: Yutong Zhang

•Apr 17, 2019

So great in general! But since it is not a pure maths course, some concepts are not explained in depth. It's a perfect course for self-learner because you can always go to the forum to look for answers.

автор: Fish

•Apr 16, 2019

Very good I learn a lot though I get confused in Week 4 about E @ TE @ inv(E). Thank you profs!

автор: Ivan Kravtsov

•Apr 14, 2019

Great instructors and great engagement. A very comfy way to have a broad view on a linear algebra.

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