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Вернуться к Mathematics for Machine Learning: Linear Algebra

Отзывы учащихся о курсе Mathematics for Machine Learning: Linear Algebra от партнера Имперский колледж Лондона

4.7
звезд
Оценки: 9,563
Рецензии: 1,928

О курсе

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....

Лучшие рецензии

PL
25 авг. 2018 г.

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.

CS
31 мар. 2018 г.

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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251–275 из 1,919 отзывов о курсе Mathematics for Machine Learning: Linear Algebra

автор: Liam M

4 апр. 2018 г.

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: Rohan A

9 июня 2020 г.

Great course guys! I have done a course on Linear Algebra in my university and watched the 3Blue1Brown series on Essence on Linear Algebra. This course was a good recap of the concepts and their applications in machine learning

автор: Ramy S R

27 сент. 2020 г.

Excellent course. Material is explained thoroughly through concise short videos with plenty of visualizations that make linear algebra intuitive. Assignments are chosen carefully and the curated python labs are very enjoyable.

автор: Prateek K S

28 мая 2018 г.

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: Himanshu G

13 июня 2020 г.

Thank you for designing such a wonderful course. I find difficulty in understanding the concepts related to eigenvalue and eigenvectors and Page Rank. Otherwise, the other concepts have been beautifully explained. Thank you!

автор: Neelam U

14 июля 2020 г.

I really enjoyed the application of the abstract mathematical concept to real-world problems. This shift from conventional teaching of the subject makes one realise why math is at the core of all technological developments.

автор: Liu Z

6 мая 2019 г.

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

18 июня 2018 г.

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

22 мая 2019 г.

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: Aldrich W

17 авг. 2020 г.

Love this course! Prof. David Dye is exceptionally great at explaining these concepts and the British accent also promotes my learning substantially. I'll definitely take the second course in the specialization very soon.

автор: Christopher R

13 апр. 2020 г.

Excellent intuitive course in linear algebra. I had no idea how much I missed during my undergraduate studies. I think I went through this twice and might go through it once more. I would love another course by these two!

автор: Nacir

22 июня 2019 г.

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: Vashista V

15 мая 2020 г.

I can now look at Linear Algebra in a completely fresh perspective from an application stand point. The course was neatly mapped out and I really benefited from the excellent content provided by Imperial College London.

автор: Christophe L

12 апр. 2020 г.

Wonderful Diploma, amazing teachers.

Even a guy like me, a medical doctor socialized in Emergency Medicine, enjoyed a lot this course;

I can't wait to attend to Multivariate Calculus.

Thank you much for your amazing work

автор: Huang X

12 мая 2018 г.

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.

автор: Lee T C

27 нояб. 2020 г.

Very intuitive and visual explanation of Linear Algebra compared to traditional math courses. This course focuses on the intuition and understanding of the fundamental concepts, rather than tedious rote calculations.

автор: Katyaini R C

12 окт. 2020 г.

I liked every part of this course. Yes, I'll need to practice to make the concepts sit better in my head... perhaps re-visit some of my 11th/12th grade textbooks as well. But it was a better starting point than most.

автор: Vinitha M R

27 сент. 2020 г.

Thank you all instructors for the efforts undertaken to develop such highly informative lectures with amazing graphics. It was really enlightening to visualize the various concepts we had studied during our academics

автор: Naveen D

7 июня 2020 г.

Awesome course for linear algebra basics. I was able to visualize the subject and see how the concepts can be applied to real life applications. The videos were short, interesting, and informative. Great instructors.

автор: Harshit L

7 апр. 2020 г.

the basic concepts give you the right intuition on how things work in vectors and matrices.

I strongly recommend this course to the beginners in this field. especially the week4 and week 5 concepts are really helpful.

автор: Aero M

11 мая 2019 г.

Very good course for building your Linear Algebra foundation. If you are starting with Machine Learning then you should surely go through this course to build your intuition about what is happening behind the scenes.

автор: Salem A

14 июня 2020 г.

A simple background on linear algebra is useful to complete the course. It is not necessary, however, because the instructors are going to teach you everything you need in a clear and concise manner which was great.

автор: Sanjay G

7 мар. 2020 г.

If you are thinking/looking to do your career in machine learning or want to brush up your linear algebra concept and how this is used in Machine learning, then this course must consider or added to the to-do list.

автор: Linc T J J

29 нояб. 2019 г.

i really enjoyed the short and concise lessons and the notebook exercises to summarise and put to practice all the learning after the end of each week. Content is easy to understand and follow。 Thank you Imperial!!

автор: Saurav K

4 янв. 2019 г.

This is an amazing course. With right blend of assignments and quizzes . The lectures are very clear and nice examples have been used to simplify the content to an extent that any one can easily grasp the concepts.