What did you learn in Mathematics for Machine Learning: Linear Algebra? | Coursera Community
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What did you learn in Mathematics for Machine Learning: Linear Algebra?

  • 9 August 2019
  • 12 replies
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What did you learn in Mathematics for Machine Learning: Linear Algebra?
Userlevel 7
  • Community Manager
  • 1058 replies
The course of the week is Mathematics for Machine Learning: Linear Algebra taught by Imperial College London.

@Claire and I are hoping that together we can help people find great courses through the community. Every week, we're featuring a course and inviting people who have taken the course to share their course highlights and how they're using what they learned.

Have you taken Mathematics for Machine Learning: Linear Algebra?

What did you like about it?

What were the key skills and knowledge you gained from the course?

Who would benefit from taking this course?

What have you done with what you learned?

12 replies

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Have you taken Mathematics for Machine Learning: Linear Algebra?
I've completed the entire specialization

What did you like about it?
The instruction was clear, to the point and accessible. The topic is very relevant for anyone looking to get into Data Science generally and machine learning specifically.

What were the key skills and knowledge you gained from the course?
The use and transformation of matrices, vectors, important underlying concepts in statistics and machine learning.

Who would benefit from taking this course?
Anyone looking to understand machine learning techniques beyond a merely applied perspective. I've taken this specialization before taking specializations in both machine learning and deep learning. Without this course, they would have been much more difficult.

What have you done with what you learned?
I started working as a Data Scientist. I expect to do more studying in the field of Linear Algebra in the future.
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What did you like about it?
The tutors ability to explain the course material in a way that is relatable, the intermittent practice quiz, I find that the more practice quiz I take the better my understanding of the course.

What were the key skills and knowledge you gained from the course?
Mathematical concept need for my entry to Machine Learning.

Who would benefit from taking this course?
Anyone interested in machine learning but does not have the mathematical foundation.

What have you done with what you learned?
Apply the knowledge gained to intermediate level course in machine learning.
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1. Course introduced me linear algebra and it's graphic interpretation, neural networks, partial differential equations, and back propogation. Along with jacobians hasiens and other things I don't recall.

2. The exit barriers are acceptable.( You feel you have achieved something after the submission). Tests also reinforce your learning in very different ways.

3. Thoughtful content.


Who will benefit? Anyone who wants to learn.


What did I do with it. Nothing much directly so far. But it allows me to think better when I use some of these techniques. Also I created a distribution skew function inspired by panda skewing.
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[Disclaimer : I have not yet finished the course , but i am on week -3 /5 ]
What did you like about it?
* I am into deep learning and machine learning techniques since few months and started learning privacy preserving machine learning through Facebook Udacity scholarship course and i found that Maths was one of the essential areas which , if not strong, can not take you much farther in the field of machine learning.
So, i searched all over but couldn't as best course as this coursera course by Imperial College london.
I liked all the content of the videos and importantly the quizzes and specifically in -between the videos quizzes.

* What were the key skills and knowledge you gained from the course?
Since, i am still learning , but i can say prior to taking this course, i was not really understanding the applications of linear algebra in real life practices of data science or machine learning or in general the applications of specific topics like why actually we need to learn about dot products of vectors, projections and orthogonal basis of vectors and all those stuff have eventually great applications.

*Who would benefit from taking this course?
Anyone , literally , even a high school student or a college student , be it interested in machine learning or data science or not.
But yeah it is a must for everyone thinking to start machine learning or data science.

What have you done with what you learned?
*I have not started any project type thing, but yeah it is helping me to understand other courses that i am taking on machine learning and deep learning.
Soon, i will start a project based solely on this linear algebra including some data science stuff that i will post about it later.
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Best part about course is consistent quizzes and assignments to check whether you understood concept or not.If I failed to answer correctly in quiz I did revisions of lectures which helped me clarify my unattended doubts even better.Teachers delivered lectures to the point covering all basics of linear algebra needed for understanding machine learning concepts.If you have no background in linear algebra this course is suitable for you! Thank you team for providing this course! I had some basic understanding of linear algebra so attempting this course was nice revision and some additions to it.
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I am from a Maths background and have taken Liniear Algebra course in my college where I was taught most of the conepts that are in this course. But what really stands out for me in this course is the way the instructors were able to realte everything with real world. Beside just solving problems, I was able to perceive the real use case and ofcourse the quizes helped a lot to digest those concepts. Overall it is a great course to lay a strong foundation for a career in machine learning.
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I liked the intuition parts where I could finally understand some concepts as eigenvectors and eigenvalues that lacked in my college times. The quizzes helped a lot as they are numerous and really get to the point. The lecturers are very good and so are the video producers who gave them support to a nice teaching environment.
Userlevel 6
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Hello everyone,

I love Maths, and I studied pure Mathematics & CS during at the university.

I found this specialization the best among other Maths courses to revise useful concepts and also upgrade my skills, especially this course that teaches Linear Algebra, it's essential to understand everything about matrices in order to be able to work with matrix dimensions to form datasets for Data Science tasks.
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in simple words through the maths we can learn that how actually the machine learning algo learns
if one is implimenting the M.L. through scikit library without having the intuition of how the algo actually works then its not going to be intersting its seem to be doing practical without having the theory knwledge that how the process will get executed
So maths teach us the working of the algo and creates the interest and a fun to impliment after that
thanks
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What did you like about it?

It provides clear indications about what should people know at minimun about Linear Algebra in order to gain some understanding about the math foundations of Machine Learning.

What were the key skills and knowledge you gained from the course?
I get some familiarity with the notions of vectors, matrices, their relationship to machine learning and other subjects.

Who would benefit from taking this course?
Many people. To review or to learn.

What have you done with what you learned?
Nothing for the moment.

Other comments: Difficulties.

I am "not more a young person", (age 62 🙂, and I had never seen linear algebra. It was a little hard for me to follow the course. I found neccesary to explore other materials, as Prof. Strang book, Kahn academies videos, some course support material from local universities, Prof Strang videos and so on.

A very good thing is that Coursera allows you to do the course at your own pace. I took some four monthes from the beginning to achieving the certificate, even if in the meantime I review and actually done other courses. But I think that the proposed rythm is too fast for a newbbie.

I think that for people without any familiarity with the subject more practical work, slow paced, should be proposed in order to gain comprehension in a more effective and kind manner.
It's clear that the videos are very concise, and so not so easy to follow.
I can understand that there is some tension between the size of the knowledge meant to be included and available time, and that it can be a little hard for a tottally newcomer to the discipline
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What did you like about it?
I was still new to Coursera when I took this course, and I was impressed by how engaging and accessible is the material. The aim of this particular course was to hone your intuition regarding important concepts of linear algebra as they are applicable to machine learning, and it did exactly that.

What were the key skills and knowledge you gained from the course?
I learned how to actively participate in a Coursera course, how to solve basic linear algebra problems, and where the gaps were in my knowledge which required further study.

Who would benefit from taking this course?
Anyone interested in learning the basics of linear algebra. This is a great starting point to put in the right frame of mind so you are well placed to pursue further learning if you feel you need/want to do so.

What have you done with what you learned?
I am currently enrolled in Andrew Ng's 'Machine Learning' course, about to finish week 7. I am also studying linear algebra more in-depth using the textbook recommended in the course's 'Resources' tab - Hefferon's 'Linear Algebra' 3rd edition.
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Have you taken Mathematics for Machine Learning: Linear Algebra?
I took this course about a year ago along other course in the specialization.
What did you like about it?
It focus on something that many people fear, the behind the scene mathematics! I find it useful to check my background knowledge before stepping into other the world of AI and Data Science and I suggest other eager motivated and serious learners do as well. A gap in knowledge would slow you down in this world step by step complete and check your knowledge and to do so, this specialization would be a good start for you.
What were the key skills and knowledge you gained from the course?
getting your hands dirty in this field is probably a good thing. Alongside reviewing what you know you simply reach a point that you suddenly find out that you actually wrote a machine learning program which works! That alone would make a momentum to follow this path.
Who would benefit from taking this course?
I think someone with enough knowledge about mathematics and algebra who wants to step into something more practical these days would benefit from this course.
What have you done with what you learned?
I simply followed the path! a large portion of my job is about data analysis and ML is a way to do it much more efficient and easier.

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