Chevron Left
Zurück zu Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Linear Algebra, Imperial College London

(2,758 ratings)

Über diesen Kurs

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....


von 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.

von 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.

Filtern nach:

481 Bewertungen

von Sushant Pandey

May 19, 2019

This course was every bit painful, fun and really worth the time spent on. :)



May 17, 2019

This course reviews the essential concept of linear algebra in the context of machine learning. However, it would be much better if it provided more optional exercise and reading materials.

von Vibhutesh Kumar Singh

May 16, 2019

It was quite intersting. Have studied these vector operations previously but havn't paid much emphasis on the geometric point of view.

von Philip Abraham

May 16, 2019

Excellent Instruction

von Graham Annett

May 15, 2019

Very challenging to follow instruction at times. Needs to update videos so a bit longer in order to effectively teach the content. Errors with calculations in week 3 with the Composition or combination of matrix transformations video. I've also had to utilize external resources to adequately understand what is being taught. I've taken other courses through Coursera and not had this level of frustration with the other courses.

von Colin Whittaker

May 15, 2019


von Nano Dit Nano

May 14, 2019

I'm only at the beginning of the course but the material is really worth it.

Great instructor. He makes himself very clear and easy to follow.

von Jafed Encinas

May 14, 2019

Able to concentrate and stay focused for periods of several hours, even when tasks are relatively mundane, and doesn't make mistakes. He has a high boredom threshold. Always assured and confident in demeanour and presentation of ideas without being aggressively over-confident. No absences without valid reason in 6 months. Reaches a decision rapidly after taking account of all likely outcomes and estimating the route most likely to bring success. The decisions almost always turn out to be good ones.

This Course always completes any assignment on time and to a high standard. This Course has outstanding artistic or craft skills, bringing creativity and originality to the task. Aiming for a top job in the organization. He sets very high standards, aware that this will bring attention and promotion. This Course pays great attention to detail. He always presented work properly checked and completely free of error.

von vikas chauhan

May 13, 2019

Good for basic understanding, Assignments are fine.

von Mashchenko Mikhail

May 12, 2019

Its easily one of the best Linear Algebra course. Provides a full picture of the vectors and matrix in ML