Вернуться к Mathematics for Machine Learning: Linear Algebra

4.7

звезд

Оценки: 7,574

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Рецензии: 1,513

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.

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: Rahul R

•Jun 13, 2020

I highly recommend this course to anyone who wants to build a general understanding of linear algebra and its real-world application. Both the instructors are highly capable of communicating the intuition behind every steps and algorithm to the viewers.

автор: DASGAONKAR Y N

•Jul 12, 2020

This was one of the best courses for linear algebra for a working professionals, students and researchers who want to brush up their skills as this course is very different from the regular book stuff.It was more practical with real world assignments.

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: Agamjyot S C

•Jun 04, 2020

A really nice course, I had already done a Linear Algebra module in the university. But that was mostly mugging up and not knowing what this is used for. This course's geometric interpretation of all topics, helped me a lot and give a lot of insight.

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: Jonathan M

•Apr 10, 2020

Extremely helpful. I haven't taken a linear algebra class in almost 5 years and by going through these videos it helped me regain an intuition towards the subject. The videos do a good job of tying the material back to machine learning as a whole.

автор: Cyprien P

•Jul 03, 2020

Great maths refresher content, with very useful 2D geometrics examples helping to build the intuition rather than just explaining the maths. I feel like I can understand this part of linear algebra now, and I know what to search for when I won't.

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: Randy S

•May 12, 2020

A good mix of theory in videos, simpler practice problems to reinforce the learnings, and scalable applications in Python. Very much enjoyed the course and feel like I've learned a lot about linear algebra and the applications in data science.

автор: Ryan M

•Apr 10, 2020

I very much enjoyed the content of this class. The professor for the first 4 weeks was great! The professor for the 5th week seemed to move at a slightly faster pace with less in-depth instruction. His visual aids were pretty groovy though.

автор: Shivam K

•May 26, 2020

The course is really helpful to those seeking clarity on the concepts. Week 4 and 5 really will really demand your attention. Loved every single bit of this course.

Would be glad if course would have included more visualisation to play with.

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: dhiraj b

•Apr 21, 2020

Offered the much needed perspective of linear algebra to develop actual understanding, than just solving problems without understanding why and how actual computation works. I would like to thank the professors for such a great course.

автор: Duraivelu K

•Apr 11, 2020

This course not only provided me the fundamental knowledge of Mathematics required to learn my next interested course of Machine Learning, but also helped me to kill the lockdown period due to covid-19 pandemic in a useful way at home.

автор: AKSHAT M

•Jul 20, 2020

Excellent course. Outstanding methodology. Great fun and intuition based leaning, kudos to David Dye, Sam Cooper and the ICL team. Thank you very much for bringing forward this course. Looking forward for many more courses from ICL :D

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

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