Back to Mathematics for Machine Learning: Multivariate Calculus

4.7

stars

4,029 ratings

•

715 reviews

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....

DP

Nov 26, 2018

Great course to develop some understanding and intuition about the basic concepts used in optimization. Last 2 weeks were a bit on a lower level of quality then the rest in my opinion but still great.

JT

Nov 13, 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.

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By Shahzad A K

•May 24, 2018

Great course! Builds up logically from a soft introduction to practical applications of multivariate calculus for data analytics. I no longer feel intimidated when I look at an expression involving higher order partial derivatives in multiple variables!

By FRANCK R S

•Jun 03, 2018

A very useful introduction of the math behind Machine Learning, a must if you plan to understand the algorithms used in ML, as usual the teachers are very very talented, focus is put in the essential and comprehension comes intuitively, Great Thanks!

By Christoph L

•Jan 06, 2020

A very good introductory course that is giving insightful explanations of how something is done and why. I especially enjoyed the part on gradient descent that was part of multiple modules. Very engaging instructors make learning easy and motivating.

By Ashok B B

•Feb 02, 2020

Fantastic course, got to know the underlying maths behind complex ML algorithms, which was always a grey area to me, the instructors clearly explained each topic, which is a definitely a must add on skill to your journey towards Data Science career

By Subtain M

•Jul 10, 2020

I think this is one of the best course for understanding the calculus behind the machine learning algorithms. This also helps me understanding the back propagation, which is considered to be very painful topic for people not from maths background

By Fabiana G

•Jul 25, 2019

It's challenging, specially about the week 4. But it's very possible to conclude successful. I just have high school and I finished the course with 100% of grade. My hint is: algebra is very important, but code can help you with this subject.

By Tommy R

•Mar 26, 2020

I've always felt intimidated by maths and it stopped me from really understanding machine learning and the different algorithms used. This course does a great job of making calculus understandable and demonstrates why it is so useful for ML.

By SARWADNYA N M

•Jul 16, 2020

The instructors are very enthusiastic and novel in their approach to teach which makes it very enjoyable to learn from them. Looking forward to completing this specialization and then learning more about ML and AI.

Thank you so much Coursera

By Mubasher A

•Sep 04, 2020

Excellent course. All concepts have been delivered in a great manner making things clear with good examples. Questions appear at appropriate times during the video to make sure that the concepts presented in the video are being understood.

By Srimat M

•Dec 10, 2019

standard short and crisp course. will do the job for what it is designed for. great explanations by mr. sam cooper and his visualization team at imperial. and mr.david also done a great job. overall worth spending funny jelly belly time.

By Tash B

•Sep 05, 2018

Although difficult, this course makes sense of what is happening under the hood in training machine learning models. Instructors explain things well and the assignments gave opportunities to practice. I thoroughly enjoyed this course.

By Badri A

•May 15, 2020

The thing I love about this course and the previous one, is how they make these heavy equations and stuff that we learn in school and university meaningful.

The instructors are very good, and the topic was handled perfectly. Well Done !

By Jafed E G

•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

By Jonathan F

•Jul 29, 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.

By Saikat C

•Feb 14, 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.

By Zixuan Y

•Jan 27, 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 ;)

By Nigel H

•Apr 18, 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

By Mike

•Jan 13, 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.

By zohair a b

•Jun 02, 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.

By Olivio A C J

•Oct 19, 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.

By Rafael E

•Aug 21, 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.

By jie

•Jun 11, 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.

By Sagar L

•Mar 12, 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.

By Nataliya M

•Nov 17, 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!

By Loay W

•Aug 01, 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.

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