Back to Mathematics for Machine Learning: Linear Algebra

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10,703 ratings

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2,127 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....

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

CS

Mar 31, 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 Amer M

•Jun 28, 2020

Good Course, It shows Linear Algebra from different perspective. You should not be good in math because the math is not advanced here, but you should be excellent in Python.

By Alek C

•Dec 7, 2020

This course is can be challenging but the material is good and i trust this over a university program since this course is applied concepts and works with lite programming.

By Anis Z

•Apr 19, 2020

It really was useful, but need a little bit more concentrated discussion forums. cause many questions were still unanswered there.

but thank you all, it really helped me :)

By JAY C

•Jun 30, 2019

I think the concepts are explained clearly, with ample examples to real life. Much easier to understand this time around. The coding labs could use more pointers though.

By Victor D G M

•Jul 14, 2020

it is quite intuitive, the programing tools are easy to use. I think its perfect for those people who have some math background but don't want to go deeper into theory

By Jaumir V D S J

•Mar 17, 2019

The course by no means replace a full semester course on linear algebra, but it´s useful for those who had already had a course on L.A. years ago and want a refresher.

By David G R A

•Mar 4, 2021

All the lectures were great, and pretty straightforward, but, some programming assestments were too dificult and not so useful in order to help to grasp the concepts.

By João M G

•Jul 28, 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 Artur A B

•Sep 29, 2018

Brushes over a lot of relevant topics, but if you want something more in-depth, definitely take something on Khan Academy, or your local university/community college.

By Caroline S G

•Jan 12, 2022

The video instruction was great but the assignments were difficult to understand given the instruction. Also, the python often failed due to unrecognized routines.

By Scott A

•Apr 22, 2018

Excellent introduction to linear algebra fundamentals, even with little/no prior exposure. Familiarity with programming concepts makes later exercises much easier.

By Nikhil K

•Apr 22, 2020

It is a good course for beginners and to revise the concept. I feel the lack of explanation in some concepts which could have been avoided by providing references.

By Karthik

•Apr 19, 2018

Short and fast crash course for brushing up linear algebra. Its better to take Khan academy tutorials and then start this to finish fast and understand the logics.

By Mazhar A

•Nov 29, 2021

Found this course very interesting, as it has good concepts regarding use of applied mathematics in machine learning. Looking forward for more interesting courses

By Mohit P

•Apr 18, 2020

the course is good, there should be more detailed explanation of the topic, the assignments are good and keep our learning at top notch. Thank you so much team.

By Shader

•Oct 13, 2018

Some material not covered well enough to pass and I am a pretty proficient student. Some material seems a little out of place with the progression of the course

By HARISH A

•Dec 22, 2018

Gives a good intro to some of the basic linear algebra - however would have been happier to see more details in the handling of eigen vectors and eigen values.

By NGUYEN B D T

•Feb 16, 2021

A good summation of Linear Algebra. This course did give an insight into what matrix is, space, and how to apply transformation in a way that is the easiest.

By Sagar L

•Feb 15, 2020

The course is pretty helpful as a recap for linear algebra and has nice explanations to set up your intuition for the core mathematical concepts of the field

By Andy T

•Aug 22, 2020

This was a great course, however you should expect to have some foundation in linear algebra to begin with and use this as a supplement to your knowledge

By Achal C

•May 19, 2020

Great course !! though not it deals everything in depth or covers wider topics it definitely helps with the basics and introduces well to the subject ...

By Haris F

•Mar 18, 2021

I am very excited to pass this course. The explanation very great but it very hard to achive this certificate in a week. But alhamdulillah i can do it.

By Avery W

•Nov 4, 2019

This is a great course, but some of the quizzes are quite difficult. If there were more explanation on the quizzes, this course would be just perfect!

By Richard E F

•Aug 25, 2020

An intersting course. It was let down by the fact that there was no involvement by the staff in answering students questions as far as I could see.

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