Volver a Mathematics for Machine Learning: Linear Algebra

4.6

2,602 calificaciones

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458 revisiones

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....

por 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.

por 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.

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455 revisiones

por 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.

por Tirthankar Banerjee

•Apr 20, 2019

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

por 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.

por rasheeq ishmam

•Apr 19, 2019

Should go more in details.

por 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.

por 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!

por Ivan Kravtsov

•Apr 14, 2019

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

por

•Apr 14, 2019

Excellent

por Anuj Nagpal

•Apr 11, 2019

The course gives you all the intuition behind all the major linear algebra concepts one needs to know

por João Carlos Lima Selva

•Apr 11, 2019

The course is very good, almost perfect for my purposes. I liked specially the effort to make the students get the necessary intuition instead of pushing a lot examples as many other MOOC usually do. But I've noticed some negative points. I ask you to take my critic as a sincere effort to improve the course and eliminate some mistakes that really matters to the students. The last quiz seems quite disconnected with the lectures and there isn't a support guide or tutorial not even a mentor answering the questions in the week 5 forum. Some mistakes on videos (eigenvalues and eigenvectors) were confirmed by the lecturer but never corrected. Not even a errata on resources section. Talking about the resources, I think it is very poor. Cousera has many better examples.

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