Back to Mathematics for Machine Learning: Linear Algebra

4.7

3,766 ratings

•

677 reviews

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....

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.

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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By João M G

•Jul 29, 2019

The course is a good review of linear algebra for machine learning. But It would have been better if there were more code exercises and if they were more challenging.

By Deleted A

•Aug 04, 2019

Strong basic preparation, but I feel that it stops too short. There should be a module 6 and a module 7 covering intermediate-level topics.

By Weiyu G

•Aug 12, 2019

It is really intuitive and good for people who have little idea of Linear Algebra. The best part is the PageRank Algo.

By Gabriel L S

•Aug 12, 2019

I like the the structure of explaining the theory using examples (in this case, geometric/visual examples). However, I would love to have further understanding on the basic linear algebra topics (or at least be linked to websites that explain this further) to allow flexibility to students like me who has zero knowledge on linear operations. Overall, I was able to overcome the challenged through self learning, understand the concepts well, and appreciate the applications in machine learning.

By Kitty

•Jun 12, 2019

Generally great course. Explanations are very clear. Cons: ① no textbook/slides/reading materials etc., have to take notes and screenshots for every single thing you want to record. ② The content is not enough. Way too less knowledge covered than college-level linear algebra course. I took this course to refresh my knowledge and it turned out that more than half of the contents are the ones I still remember.

By Gurudu S R

•Aug 18, 2019

1.Need more clarity on calculating Eigen vectors using back substitution of Eigen values.

2. Power Iteration method for the Page Rank Algorithm should be more specific and clear.

By Priadi T W

•Sep 07, 2019

The course was great for me. It opens up new perspective to some vector and matrix application. However, I must admit that you must have strong background with math before taking this course, as I was little bit struggling with matrix part.

By Alexander D K

•Aug 21, 2019

Fairly good introductory course but not a substitution for a proper LA course for ML purposes.

By Jean S

•Aug 20, 2019

Excellent course and very practical; it's really focused on machine learning and there's the opportunity to learn some coding in Python. I would recommend it to everyone interested in machine learning. I give it 4 stars because there's always room to improve.

By Ajay R

•Sep 11, 2019

Tough course, but got better understanding of topics related to math behind real-world ML models.

By Vaibhav S

•Sep 10, 2019

can be more detailed

By Mohamed B

•Aug 27, 2019

The concepts are explained clearly, but someone who has already done some machine learning before might find some parts unchallenging

By Shubham K

•Aug 31, 2019

The course is great with really good teaching community , as a beginner it was a really good experience.

By Amit A

•Aug 30, 2019

Eigenvalues and eigenvectors while explained conceptually very well, the jump to page rank and transformations using them was bit hand wavy. May be it is not that important or the topic is too complex. I think I have to go through it multiple times to get the gist of it once again. I might if there is real applciation of it in ML.

The course is still very good and thank you for sharing it with us.

By Aditya G

•Sep 02, 2019

The course is really nice. A bit of programming experience is needed to complete this course.

By Cindy X

•Dec 21, 2018

I think this course is a little bit hard for a beginner with python. And I hope that the teacher can talk more about the Machine learning part.

By Nathan C

•Jan 26, 2019

Having no background in linear Algebra made it difficult to complete the quizzes, assignments and exams. Even with the instruction (which was good) I found the hands on portions to be different from what was being explained in the videos. I will instead have to take the key concepts and do more research on my own to fully understand them.

By Manuel M

•Jan 26, 2019

The course feels very disorganized in general. Some quizzes are about 10 standard deviations from the average difficulty, which is befuddling to say the least.

By Matt

•Feb 24, 2019

This course would be perfect if more elaboration on the maths required to complete the quizzes, was provided.

By Carlos R T G R

•Mar 19, 2019

The videos need to be updated, there are quite some errors that are already identified...

By Adam R

•Nov 16, 2018

Some of the quizzes go beyond what is in the videos and often spent ages on them.

By Fernando B d M

•May 14, 2018

Like most of Coursera's courses there are no staff members available in the forums (which is extremely shameful for Coursera - repeating the same boring pattern over the years). Don't even try it if you have never seen linear algebra or python before. Otherwise, it's useful for practicing a few concepts or refreshing others.

By Jared E

•May 26, 2018

Overall good, but some nasty difficulty with the programming assignments... especially the last one.

By Nicholas K

•Apr 20, 2018

Enough gaps that I finished feeling like I really had no idea what was going on.

By Flávio H P d O

•May 12, 2018

explanation not very clear

not enought examples