Voltar para Mathematics for Machine Learning: Multivariate Calculus

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5,171 classificações

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924 avaliações

This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future....

JT

12 de nov de 2018

Excellent course. I completed this course with no prior knowledge of multivariate calculus and was successful nonetheless. It was challenging and extremely interesting, informative, and well designed.

SS

3 de ago de 2019

Very Well Explained. Good content and great explanation of content. Complex topics are also covered in very easy way. Very Helpful for learning much more complex topics for Machine Learning in future.

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por Jafed E G

•6 de jul de 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

por Jonathan F

•29 de jul de 2018

Following on from the Linear Algebra course, this is equally excellent. Again, the main enjoyment comes from seeing techniques learnt at school (partial derivatives, Taylor series, Newton-Raphson, etc) actually being used in practice.

por Saikat C

•14 de fev de 2020

Excellent course. It provides all the math required to understand machine learning in a deeper level with everything explained. This course connects all the necessary ideas and provide a coherent view of machine learning mathematics.

por Kenneth B

•3 de fev de 2021

I found this to be a valuable overview to calculus. The course could be improved by including more explanations for answers, as sometimes I wasn't sure how a given answer was derived, but it was a worthwhile use of my time overall.

por Zixuan Y

•27 de jan de 2020

I have learnt Calculus 1 before, so this course is much easier for me than the first course in the specialization. With the notebook tool, I now know how to put derivative into python. The teachers are really good. Thanks a lot ;)

por Nigel H

•18 de abr de 2018

A change in staff from Imperial but the same enthusiasm; high standards of teaching mean you are going to get a lot from this course. Lots of examples and the practice quizzes really help with the consolidation. Great stuff.Thanks

por Mike F

•13 de jan de 2020

A great course which covers the necessary aspects in a very interesting and intuitive way. Makes really good use of graphics, rather than only pure maths, in order to give an intuitive sense of what's happening behind the magic.

por Zohair A

•2 de jun de 2020

Awesome course. I've always thought of math as a burden. Never really liked it. But this course has made me fall in love with mathematics. The way the instructors taught different concepts and put them all together was amazing.

por Olivio A C J

•19 de out de 2020

A really fine course with a correct balance between theory and practice. I learned a lot about multivariate calculus applied to machine learning. This course is really very helpful for anyone wanting to learn machine learning.

por Rafael E

•21 de ago de 2020

I've never gotten such clear explanations during my undergraduate course, even in the graduate courses that I did. Practical exercises and visual resources help a lot to assimilate abstract concepts. I strongly recommend it.

por jie

•11 de jun de 2020

Excellent course. I almost forgot everything i learned at college. I never thought I could regain all these knowledge with ease (was very painful back in college) . Samuel Cooper is probably the best instructor at coursera.

por Sagar L

•12 de mar de 2020

A really nice course in the series. Quite useful from the perspective of the back end mathematics of ml techniques like Neural Networks. Anyone who wants to work in this domain, would be more than satisfied with this course.

por Nataliya M

•17 de nov de 2018

Despite there being some mistakes in videos, this course is a nice introduction to Multivariate Calculus with application to Machine Learning and Data Science. Assignments are fairly challenging and also very interesting!

por Loay W

•1 de ago de 2019

Thanks to the simple demonstration, and the interactive learning, I feel I can interpret the math as a new

language I can read and speak with, it was an interesting and core skill to have,

as a machine learning engineer.

por Francisco R

•29 de out de 2019

Great course, I really enjoyed it. The way of teaching visually rather than theoretically keeps those that get lost in formulae like me on track. The visual explanation of what a derivative is helps a lot for instance.

por Deepak n

•3 de jan de 2019

Superb course !! This course had a lot more application oriented problems and the instructor helped us visualise the intuition behind Math ! Looking forward for more of such courses from the Imperial College of London..

por Gilsiley D

•4 de ago de 2020

Interactivity in exercises, illustration in videos explain really in a new manner advanced topics, bacaming easy to understand. I amazing all. I recommend, but you need have a good understand in algebra and calculus.

por Mohammad A M

•7 de nov de 2019

This course is really the BEST course for learning Multivariate calculus. Believe me, if you want to be an expert machine learning or data scientist, this course is crucial for you. Thanks a lot to Imperial College :)

por Jimmy K A

•3 de fev de 2019

This is a great course to brush up your machine learning maths, this course describes backpropagation nicely and how its derived. Large part of this course is focused on optimization in which calculus is mostly used.

por Sertan A

•15 de nov de 2020

It requires lots of outside research but in the end, it worth the effort! Also, I felt an absence right after finishing this course. I feel like I had the chance to understand the glimpse of lecturers' perspective!!

por Deleted A

•26 de jul de 2019

I really like this course. It goes over the multivariate calculus concepts and explains them in a very intuitive way referring to the univariate case. it's still very easy for people have seen the material before.

por Arthur T R

•11 de out de 2018

Excellent for reviewing the main concepts of Matrix Algebra and learn some few new topics. This course also gives a great intuition on how things work graphically, which increases our understanding of the topic.

por Muhammad B

•9 de jun de 2021

It's a very great course with a whole great skills.

but at last 3 weeks, the jump to the new topics was a bit hard and I had to spend much time looking at other materials to make sure that I understood the topics

por Phuong A V

•2 de ago de 2020

It is very challenging course. I have to watch the video many times. But the explanation in the practice quiz is very useful. It helps me to review the lectures and try some complicated function. No pain no gain

por Helena S

•28 de fev de 2020

The best course in the specialization. The material is very abstract, but the Lecturer's explanations are so clear that it seems easy - even though it isn't! A model course. I've learned a great deal. Thank you!

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