Back to Mathematics for Machine Learning: Linear Algebra

4.6

(2,764 ratings)

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....

By 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.

By 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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485 Reviews

By 刘佳欣

•May 23, 2019

This is an incredibly great course for linear algebra. Thank you so much for the neat and elegant explanation! Highly recommend it if you focus more on calculation without knowing the meaning behind matrices and vectors in your past linear algebra journey. Thanks a lot dear professors!!

By Fabricio Oliveira

•May 22, 2019

Great pace and content very nicely curated. Loved it and will carry on with the specialisation. I am a professor myself and I am also learning a lot about good practices when it comes to teaching. Could not recommend more!

By Fuad Efendi

•May 22, 2019

It is a little messy: there are no clear definitions of Vector Space, Normed Vector Space, Euclidean Vector Space. Functions as COS and SIN are used to show basic concepts, orthogonal base, and so on. "Projection" concept always relies on base being orthogonal, projection being under 90 degree (what is 90 degree in vector space?), and space being Euclidean, although it is much simpler and applicable for just Vector Space (space without "norm" defined). Good introductory course for high-school; bad for University. Good for kids who just finished learning Pythagoras Theorem, SIN, COS, and basis of Euclidean geometry. Example of house (with number of rooms which is positive Integer number, and price which is positive Decimal) is not really a vector. Examples of non-Euclidean spaces and their applications in machine learning not provided (geometrical deep learning on graphs for example). Basic course for those completely unfamiliar with what "vector" is. Provided tests in Python are confusing because in the context we write vectors (and "base" vectors which matrix consists from) vertically, and in Python - horizontally. For example, [[1,2],[3,4]] is matrix, but it won't transform base vector [1,0] into [1,2]. This is confusing and should be mentioned before test begins.

Thank you for helping me to recall this knowledge. I finished three weeks; I may need to update review later.

By santhosh sundaram s

•May 22, 2019

The course is very good only thing is you have to spent some quality time to understand in deep why linear algebra is more important and how does it involved in machine learning.

By Sushant Pandey

•May 19, 2019

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

Thanks!

By JUNXIANG ZHU

•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.

By 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.

By Philip Abraham

•May 16, 2019

Excellent Instruction

By 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.

By Colin Whittaker

•May 15, 2019

Excellent

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