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Retour à Mathematics for Machine Learning: Linear Algebra

Mathematics for Machine Learning: Linear Algebra, Imperial College London

4.6
2,176 notes
381 avis

À propos de ce cours

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....

Meilleurs avis

par 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.

par 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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380 avis

par

Feb 20, 2019

The best linear algebra courses I ever learnt!

par muhamad rausyan fikri

Feb 20, 2019

easy to understand

par Bryan Stafford

Feb 19, 2019

A great introduction to linear algebra!

par Camilo Jara

Feb 18, 2019

Great class and wonderfull material. Focused on intuition and programming rather that minndlessly solving problems as a mechanical challenge.

par Andrew Khuhlin

Feb 17, 2019

great visual explanations of concepts, but the course could have been more informative

par Prashant Dabholkar

Feb 17, 2019

Good explanation. Some of the exercises and quizzes need a deeper understanding of the course content

par David Bernal

Feb 16, 2019

The video approach to this course is really amazing. The visuals presented and the ease in understanding touch mathematical concepts made this course fantastic to take. Although I would have preferred more challenging quizzes and programming assignments the material taught was still world class.

par gregorius airlangga

Feb 15, 2019

Very Good Math Lecture!

par Yevhenii Sharov

Feb 15, 2019

Very good course. It well structured, good lectures and assignments. It gives enough intuition and refresh to move forward. Thank you team!

par adam mcquistan

Feb 12, 2019

reasonably well constructed and presented material