Back to Mathematics for Machine Learning: Linear Algebra

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10,261 ratings

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2,060 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....

EC

Sep 9, 2019

Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.

HE

Aug 8, 2021

the instrutors were too good and their explination for the concepts was to the point and it made me realize things in linear algebra I didn't know before although I studied it in school of engineering

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By Aurel N

•May 8, 2020

Intuitive geometrical representations of eigenvalues and eigenvectors in 3blue1brown style. Had some concerns with a few theoretical inaccuracies of the material presented.

By Akeel A

•Jul 22, 2020

It was a good to review linear algebra again and see how what I learned in my first year course at university could be applied here! Plus it was good to see Python again.

By Manuel M

•Jan 25, 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 itwipsy17

•Feb 25, 2020

It is good course for machine learning. But I didn't fully understand the page rank system with damping.

More explanation of damping is needed for the newbie.

By vignesh n

•Sep 12, 2018

Transition from explanation of basic to advanced concepts could have been better. There was an assumption that few things was already know to the learner.

By Alexander D

•Aug 7, 2018

Not enough focus on how material connects to machine learning. A case study example would help, as would a very slow, detailed step-by-step illustration.

By Santiago M

•Sep 14, 2020

Nice one. But realized I needed more foundation on this matter. So decided to abandon and level up my topic knowledge in Khan Acadamy. I will be back.

By Cindy X

•Dec 20, 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 Christos G

•Jan 24, 2021

Very good explanations on difficult subjects but a bit short coverage of various cases, thus some assignments and quizzes were challenging.

By Atish B

•Sep 24, 2020

Answers to Several questions in Week 5 quiz around eigen values and eigen vectors need to be revisited as they donot appear to be correct.

By Serdar D

•Feb 15, 2021

This course consists of very fundamentals of linear algebra. I expected advanced linear algebra contents and more software applications.

By Amal J

•Jul 15, 2020

The course gives a good beginner-friendly Introduction to Linear Algebra. But the courses could cover a little more topics in LA.

By Jorge G

•Aug 14, 2020

I would give it 3.7, examples are good but the vectors the lecturer draw were no easy to understand because of drawing by hand.

By Badri T

•Dec 29, 2019

The Eigen system could have been better explained. The last quiz was too hard and the concepts required were not covered

By Aaron H

•Oct 17, 2019

Lot of the concepts seemed glossed over and could have used more guided practice and/or linkages to real world problems.

By Indira P

•Mar 7, 2021

It is so complex and contains so much knowledge but hard to understand for beginner or intermediate in mathematic

By Kate G

•Nov 19, 2020

The instructor is skipping a lot of material and the quizzes require working with external sources to be solved.

By Matt P

•Feb 24, 2019

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

By N s n r

•Dec 11, 2019

i expected a practical mathematic approach rather than only mathematical approach.but page rank algo is good

By Viktoria

•Oct 11, 2021

The course could give more insights on a number of topics and it was very brief which made a bit frustrated

By Tony M

•Oct 23, 2020

Good course, but could add extra steps for those a little rusty with algebra, matrices, vectors and so on.

By Jared E

•May 26, 2018

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

By Almir B

•Oct 15, 2020

The programming part is too confusing for someone that is just starting.

Thanks for the opportunity.

By Aniket D B

•Aug 9, 2020

Everything is good. But I still don't have any idea about how I will use this in Machine learning.

By Alberto M

•Apr 4, 2019

Good material if you want to refresh your knowledge, poor programming assignment support/feedback

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