Вернуться к Mathematics for Machine Learning: Linear Algebra

4.7

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Оценки: 5,144

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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....

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.

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.

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автор: Anna U

•Jan 14, 2020

An excellently simple explanation of concepts of linear algebra. Applause for lector. I really liked this course and found it very useful for those newbies in machine learning like myself. I recommend this course to all my friends and others interested in.

автор: Loc N

•Jan 09, 2020

Awesome course! Entertaining and digestible, with great assignments despite some hiccups in file organization and a slight lack of response from admins (understandable because the course is old). It was so awesome that I had to go and tweet the profs.

автор: Lorenzo

•Sep 27, 2019

it's a very well structured and well taught course. The lecturers have the ability to keep the students interested in the subject and the various exercises at the end of each session are a very good way to find out where extra work/research is needed.

автор: Volodymyr C

•Jan 27, 2019

Clearly explained and key equations are derived with good step sizes. Quizzes and assignments are challenging (which is good!) and have high expectations for learners (which is really good for my motivation). Overall, I am really enjoying this course.

автор: Nuthakamol

•Mar 25, 2020

The presentation an way of teaching is excellence; however, the course should add more reference or additional source or materials for more in dept detail for the person who feel that the simplified explanation in the course are still not sufficient.

автор: Astankov D A

•Mar 16, 2020

Great explanation of all the important things, with topical examples and practical tasks. Still, it seemed to me that the course was growing more and more complex exponentially by the end of it, so it was really hard to catch up starting week 4.

автор: Thomas F

•Apr 19, 2018

Highly valuable introduction to linear algebra. Maybe the programming assignments are far too easy, while some of the quizzes definitely are hard. And the best part of the course was to introduce www.3blue1brown.com with it's videos on youtube.

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автор: CHIOU Y C

•Jan 03, 2020

This is a good linear algebra course intro. May not be the one for who is looking for mathematical rigorous but it's enough for machine learning. Linear Algebra is important but not all topics and this course highlights the needed materials.

автор: Jaromir S

•Sep 30, 2019

I needed a quick refresh of my prior knowledge of linear algebra for my MSc course and I wasnt disappointed. I also appreciated the complementary python exercises and the effort to put the material into a context of a real world application.

автор: Someindra K S

•Jan 03, 2019

I got a lot of intuition about some fundamental aspects of linear algebra. Rest of courses on maths was very rigorous in terms of methods. This was more inclined towards applications in machine learning. I enjoyed the entire learning process

автор: Stefan B

•Apr 08, 2018

It was fun to work through the course. Sometimes it was challenging as it has to be. Now I have a much better understanding of the topic. Especially appreciated is the approach of the instructors to build intuition: it worked for me, thanks!

автор: Danilo d C P

•Jul 19, 2019

I really enjoyed taking this course. I could review and learn for the first time some important topics for machine learning, in special the eigenvalues and eigenvectors classes. I'd like to thank the course's professors and collaborators.

автор: Omar R G

•Mar 17, 2019

An excellent course on the fundamentals of linear algebra. It was great revisiting all this topics. I would also say that some knowledge on linear algebra would be useful for taking this course given the fact that the lectures are quick.

автор: Greg E

•Jul 15, 2019

I thoroughly enjoyed this course. After using matrices and vectors for decades in my work, I have finally gained some intuition about what the dot-product operation, determinant and eigen-vectors actually represent. Thank you so much.

автор: Jafed E

•Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

автор: laszlo

•Apr 21, 2018

Awesome course!!! The course is very helpful for those who are willing to build an intuition of linear algebra. The coding assignments are a bit easy for CS students, but allow you to understand what has been taught in the course.

автор: David S

•Jun 24, 2019

Excellent. Exactly what I needed. A linear algebra course in machine learning. Top notch presentations, materials, and explanations. A nice blend of concepts and detailed calculations especially in transformations and eigenvectors.

автор: Rahul S

•Oct 28, 2019

This course is little challenging if one has not revised Linear Algebra before, but quite interesting and fun given the examples and utility only after learning the basics of linear algebra elsewhere and then attempting this one.

автор: Liam M

•Apr 04, 2018

This is an excellent refresher of vectors and linear algebra, and although I did it years ago in college I still found some new insights from doing this course. Its all explained very well without being bogged down in formailty.

автор: Prateek K S

•May 28, 2018

Nice course. This course is very good to build your fundamental knowledge for machine learning. This course gave me very clean and straight forward understand how mathematics play very important role in machine learning field.

автор: Liu Z

•May 06, 2019

As for Chinese students, this course clearly explain the vectors, vector multiplication in a graph way, which for me is very useful, instead of in many Chinese university, which just state formula of calculating the vector.

автор: Jurij N

•Jun 18, 2018

I was very satisfied with the course. I'm really grateful for the effort they put into the programming exercises, so I finally began to put the theoretical knowledge into code. From now on I am able to experiment by myself.

автор: Fabricio O

•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!

автор: Nacir

•Jun 22, 2019

Great course. The instructor is really great (and neat), communicates the ideas really well and if Imperial College London is ranked that high worldwide, it's definitely because they hire professors this good. Thank you.

автор: Huang X

•May 12, 2018

This course helps me a lot. I don't need to calculate the matrix by hand. I just need to get the concept of what is the matrix doing and use computer to calculate it. This is the most import thing I got in this course.

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