Вернуться к Mathematics for Machine Learning: Linear Algebra

4.7

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Оценки: 7,585

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Рецензии: 1,516

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: Lisa M

•Apr 07, 2018

This was a fantastic course. I'm new to linear algebra, so it was bit intimidating even signing up (!) - but the lecturers were really, really good about explaining all concepts from the ground up so it was always possible to visualize and extrapolate from solid foundations. For me it was a stretch each week, but in a good way: very challenging, but achievable with enough planning and effort.

автор: Wasif S

•Aug 03, 2020

This course for me was meant to be a habit-refiner but ended up being thinking of more into the depths of the world around. Very good course. The quiz & assignments are really good. Although some of the details are missing from particular sections, I think it will grow over time. As a Machine Learner's intro to perspective, this is really been a decent exercise to flex yourself. Goodluck.

автор: Ying T

•Mar 09, 2018

An awesome course with high quality video lectures!! I will recommend this course to anyone who's looking for a refresher or quick pick-up on linear algebra. The homework's compatible with the materials and is quite interesting. The lecturer also did a good job on explaining critical concepts with easy but good examples. I'm looking forward to more similar courses from Imperial College.

автор: Jayant V

•Mar 29, 2018

I have taken a course on linear algebra during my graduate program and must admit that it was not one of my more comfortable ones! Coming back to this course online, it really did help me get a much better understanding of concepts like dimensionality, basis, eigen values and eigen vectors. I intend to go over the lectures at least a few more times to be sure I have understood it well.

автор: SHOUNAK B

•Jul 12, 2020

I personally loved this course as it changed my outlook and perspective towards Mathematics in computing and as a whole. I really enjoyed taking this course. It got me into some difficult assignments but the joy of solving it after lots of brainstorming and discussing with peers was awesome experience. I look forward towards developing myself and gaining knowledge with these courses.

автор: Stefan R

•Apr 28, 2020

It is usefull and good course. It does require at least some familiarity with the concept or otherwise you will spend hours trying to understand how things happen, but lecturers have overall given great insights in how things work and tried to simpliify as much as possible.

Maybe would be usefull for course creator to add some optional basics tests on the topic, for total beginers.

автор: Huy M

•Mar 11, 2019

I've only done half of the course but I already know this course is one of the best on Coursera! Complex concepts in mathematics are broken down into simple terms. The professor also clearly stated what those concepts are used for in practical, which certainly help learners have a clear idea of why they are learning this course. Very exciting every time I click onto new lessons!

автор: Hardik S

•Jun 20, 2020

Not being from a Mathematics Background, one surely need best tutor I guess for understanding Mathematics that's required in Machine Learning/ Data Science. Both the tutor Sam Cooper and David Dye amazingly Explained the topics and I'm happy to have completed the Linear Algerbra Course and now moving towards other part of the course i.e Course 2 Multivariate Calculus.

автор: Ramon M T

•Aug 20, 2019

Excellent Course, I remembered the linear algebra that I saw in school more than 26 years ago (I studied applied mathematics and switched to Actuaria), but now with examples related to DataScience.

As observation.

For someone who has not programmed in some language the exercises can be challenging, but they are always very intuitive if the example steps are performed.

автор: Badri A

•May 01, 2020

At first, I was kinda of afraid of Math in general and Linear algebra in particular, but after taking this course, I am satisfied with it.

A special thanks to the instructors and all the people behind this course, for making thing simple and comprehensible, and at the same time, hit the target. Looking forward to keep learning and carry on with this specialization !

автор: Rhea B

•May 20, 2020

This course was very informative . Having learnt to solve most of this problems by hand in under-graduation , this course helped me to code these hand-worked problems . Additionally I was able to understand and visualize what the problems actually do . I highly recommend this course for anyone who is looking to learn or advance their career in machine learning .

автор: Art P

•Jun 08, 2018

This course was of high quality, was very helpful in explaining some key concepts and I appreciated the instructors energy and humor. My only complaint about the course is that some of the quizzes and homework assignments felt significantly more challenging than what was covered in the lessons; however, the discussion forums proved helpful in closing this gap.

автор: Sridhanajayan S

•May 31, 2020

This is an exceptional course for learning Linear Algebra in an intuitive way. i would recommend this course to everyone who is fond of mathematics. This course will also have programming assignments with python and numpy packages. Overall I had a wonderful experience and a handful of knowledge. Thank you for the course creators and professors and lecturers.

автор: Ollie D

•Jul 09, 2020

For someone having already graduated with a degree in Mathematics, the mathematical concepts centred around this course were easy to understand, but then applying this knowledge in to code was challenging. Which I was expecting it to be given my lack of experience with python and jupyter notes. A worthwhile course for anyone looking in to data science.

автор: David P

•Jul 10, 2018

Great content, lecture videos are brilliant. I would make one suggestion; it would be great to have more examples or even recommend text books that we as learners can download or purchase, this will assist those who wants to learn these techniques in practical examples. Other than that I have learned alot and will continue using coursera, good job guys

автор: Ahmed R

•Apr 22, 2018

This is a very good introduction and review of Linear Algebra. The particular highlights are the use of geometric perspectives to give intuition rather than just labouring through the mathematics. I also learned where I need to learn more in order. Overall will recommend either as a review or a stepping stone to learning more about Linear Algebra.

автор: Kohinoor G

•Apr 24, 2018

One of the best Linear Algebra [LA] courses for beginners/novices. It takes away the drudgery of algebra and formulae and tries to explain the "essence" of LA. This is by no means comprehensive LA course - but good enough for people who are fed up with "this is how to calculate the Eigen vector/determinant/<insert pet peeve>" mode of teaching LA.

автор: Kerr F

•Jun 23, 2020

Brilliant course which helped me to re-learn/learn linear algebra methods for machine learning! The course instructor videos, course structure, worked examples and assessments were all extremely useful and allowed me to achieve my learning goals. I would recommend this course to anyone (but would maybe first suggest brushing up on basic python).

автор: Jonathan S Y P

•Apr 12, 2020

Me parece un curso muy bueno, es básico pero la verdad hay que practicar mucho haciendo ejercicios y no conformarse únicamente con la información de los vídeos, si no, buscar otras fuentes para complementar. Para ser básico fue un desafío porque hay problemas que aparecen en los exámenes que requieren de mucho análisis. Vale la pena; me gustó!

автор: Kisan T

•Mar 09, 2020

This course has helped me to understand the basics of linear algebra and it's application in computer science. I was aware of mathematical calculations of the linear algebra, but I did not know reason and meaning of those calculations. I am grateful to Imperial College London and Coursera team for giving me opportunity to take this course.

автор: Duc D

•Sep 22, 2019

Awesome content and very clear lectures. Would be great to have links to more in-depth explanations of certain unexplained assumptions. For instance, it's unclear how the characteristic equation comes about (by assuming that the characteristic matrix does not have an inverse) and also why the page rank matrix is setup the way it is.

автор: 谢仑辰

•Feb 28, 2019

I really appreciate staff of ICL's effort to bring us such an intuitive and straightforward course. It's totally different from those linear algebra courses I've received in China. From your idea on explaining this course on space and transformation, I started to build a strong foundation about linear algebra, and machine learning.

автор: Gabriel W

•May 23, 2020

I did the 3 specialization lessons "Mathematics for Machine Learning" (Linear Algebra, Multivariate Calculus, PCA). I really had a lot of fun and learnings in the first one (5 stars for Linear Algebra): David Dye is an increadible teacher. Thank you for your enthousiastic Knowledge Transmission: Mathematics are very cool with you!

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