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Bewertung und Feedback des Lernenden für Mathematics for Machine Learning: Linear Algebra von Imperial College London

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10,261 Bewertungen
2,060 Bewertungen

Über den Kurs

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....

Top-Bewertungen

EC
9. Sep. 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
8. Aug. 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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251 - 275 von 2,067 Bewertungen für Mathematics for Machine Learning: Linear Algebra

von Aleena T

13. Sep. 2020

Excellent course for anyone who wants to know the nuances of Linear Algebra and its applications.The applications are not just mentioned,but one gets hands-on experience applying the concepts they learned,in code.Hats Off to the entire team!

von Shivam K

26. Mai 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.

von CHIOU Y C

2. Jan. 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.

von Jaromir S

30. Sep. 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.

von Someindra K S

3. Jan. 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

von Stefan B

8. Apr. 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!

von Souvik G

8. Feb. 2021

I have never visioned mathematics the way it was taught here. I believe every Engineer may he/she be a an ML engineer or not must take this course to just fall in love of mathematics. This course will inherently motivate you to dig deeper.

von Danilo d C P

19. Juli 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.

von Omar R G

17. März 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.

von Felipe C

29. Nov. 2020

Very good course. I liked it a lot. Some abstract thinking required. The last week is a bit less well explained but OK nonetheless.

In my experience, the estimated times for completing the work are a bit optimistic, it took me more time.

von Abdul-Rashid B

6. Jan. 2021

Great lecturers, excellent delivery of subject matter. This course did not disappoint me. It provides a concise yet in-depth revision of linear algebra as is relevant to machine learning. Looking forward to more from these instructors.

von dhiraj b

21. Apr. 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.

von Duraivelu K

11. Apr. 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.

von AKSHAT M

19. Juli 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

von Greg E

15. Juli 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.

von Jafed E G

6. Juli 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

von Mark A C

22. Nov. 2020

This course has provided me a better understanding of linear algebra concepts specifically on how eigenvalues, eigenvectors, matrices, and vectors can actually be observed or used in engineering (or even in day to day) applications.

von Vijayakumar

20. Mai 2020

It was a very good learning and I enjoyed a lot. Hoping to take the advanced level courses in Machine learning and related areas. Thank you very much Professors David dye, Samuel J Cooper and A Freddie Page. Hoping to see you again.

von laszlo

21. Apr. 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.

von Laura-Jane D

13. Mai 2021

An excellent introduction to the core linear algebra concepts needed to understand ML. I especially enjoyed the emphasis on how matrices transform vectors. It provided me a much stronger intuition of the geometry than I had before.

von David S

24. Juni 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.

von Shwetha T R

14. Sep. 2020

I loved this course! Both Prof David Rye and Prof Sam Cooper were amazing and used brilliant techniques to ensure creative learning. I enjoyed the eigen vectors and values and pagerank algo module a little too much! Thanks a lot!

von PATHIRAJA M P H S

12. Juli 2020

The course contains very creative introductions to some of the linear algebra theories that I was already familiar with. Could get new intuitions and better, deeper understanding of those concepts. Really glad I took this course.

von Mohamed S

26. Juni 2020

I liked the course and huge number of exercises. Maybe my only problem is the academic form of the lectures that makes me lost sometimes and forces me to google for an Indian guy who can teach me the concept in a more easier way.

von Rahul S

28. Okt. 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.