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Learner Reviews & Feedback for Linear Algebra for Machine Learning and Data Science by DeepLearning.AI

4.5
stars
1,544 ratings

About the Course

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful. After completing this course, you will be able to: • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. • Apply common vector and matrix algebra operations like dot product, inverse, and determinants • Express certain types of matrix operations as linear transformations • Apply concepts of eigenvalues and eigenvectors to machine learning problems Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works.  We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use....

Top reviews

AM

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it's was a great experience and the explanation was easy to understand. I want to thank everyone who works on this course. but I have an suggestion that labs supported with visual content like videos

PA

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Best Visual Explanation, I've got new thinking of the same things which I had learned in the Past. It great Course Thanks for making Such Amazing Content.

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351 - 375 of 408 Reviews for Linear Algebra for Machine Learning and Data Science

By Dev K

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Jul 24, 2023

Great theory and maths. Programming portion can be improved.

By Mini I

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Jul 3, 2024

very good explanation and practical part also very useful

By Kareem W

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Oct 28, 2023

Some of the material could have been explained better.

By Hemanth R K

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Jun 2, 2023

programming concepts should be explained more better.

By Guru P S

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Jan 10, 2024

Proper Base and path for learning linear algebra

By sitsawek s

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May 21, 2023

in week 4 not completely clear about context

By Olivier D T

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Apr 16, 2023

Very clear, greatly enjoyable! Thanks a lot!

By David K

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Aug 13, 2024

Really good explanation of topics like PCA

By Aleksey C

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Jul 28, 2023

More exercises would be beneficial.

By InFluX

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Jul 25, 2023

Last week is a little confused.

By Aymen K

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Aug 7, 2024

is too hard for me but i pass

By Xiang Z

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Feb 6, 2024

it's so deffcult

By Abdullah M

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Feb 29, 2024

It is too basic

By Basil E

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Nov 2, 2023

good

By M. R R

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Sep 25, 2023

good

By Abhinav J

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Sep 23, 2023

good

By Hau T

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Apr 1, 2023

First, I'm a non-native English speaker, the language barrier is really a tough challenge for me to learn this course. However, I think this course is created for international learners, so these problems should be solved: - Luis's teaching is good. But I found there are some missing information or formula needed to solve the exercise. I have to Google a lot, which mean the course is not covered well.

- Exercises are really confuse sometimes. And it's not only me, I checked and many student got confused problems on Community.

- I would love to have more illustration. Some visual effect to point out which part Luis is talking about in the presentation is also good to have. Once again English is not my primary language, and Math is a lot of numbers, symbols and a lot of terminologies. I'm pretty sure even native ones may get lost too.

- I wish there is a Discord server for students, because it's usually quicker to get response there. The community page's UI/UX and navigation is not good. - Put the Notation on top of 4 modules. Why putting it at the end? I wish I knew it earlier.

By Anil K

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Feb 2, 2024

1- Few topics I could not understand like they asked a 3X3 matrix question in the assignment but it was not discussed 2- Some assignments don't have clear instructions. Example Week 4: Question 7. 3- QnA is fine but its a bit delayed like we ask question on stack overflow and someone will answer when they see it. I think given the amount of money we are investing in this course there should be dedicated live QnA sessions. Then I can go for a 4 star rating. If i am supposed to just see videos why cant i just see it on youtube. 4- My certificate says Coursera learner instead of my name. I was expecting that to be my name.

By Kayce B

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Mar 28, 2024

The course was a success in that I have much more intuition about linear algebra now and I was able to get farther in this course than my previous attempts to learn linear algebra. Things I didn't like: the increase in difficulty in week 4 was a bit ridiculous, all of the programming assignments were "fill in the blanks" whereas I was hoping to build some stuff from scratch, and it's not clear to me how some of the later course material is applied in ML.

By Kayvon P

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Sep 29, 2023

The course was solid right up until the last week, when the material on eigenvalues and eigenvectors started to feel very rushed and poorly explained. Some questions on the quiz for this section were extremely hard to interpret as a result. Worst of all, the course did a poor job of explaining why eigenvalues and eigenvectors are important for machine learning. Aside from these shortcomings, the course deserves at least 4 stars.

By Stefano E C

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Sep 1, 2023

Visual examples are nice, but this is maths and IMO the course lack of some match way to be added.

Also, some parts of the labs problems, for graduation or not, have ambiguous parts some more words or examples can help.

I suggest take a pure linear algebra course in place of this, and after going for a machine learning course.

By Dr. O K A

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Apr 6, 2024

It's a decent course but extremely easy. The labs are merely fill in the blanks with basic programming. Hardly covers any machine learning algorithms, a basic neural net and PCA are all you will encounter. It is still a nice refresher if you need to review basic Linear Algebra concepts.

By Omar M H

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Apr 18, 2024

The programming assignemnts are very poor, the topics are simple but at the same time not explained in depth enough, especially week 4 is extremly chaotic and makes no sense. also how this is actually used in the real world of machine learning is extremly poorly presented

By Jim C

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May 19, 2023

The course assumed a lot of prior knowledge or perfect understanding; I found myself looking to Khan Academy for deeper explanations of many of the topics. Either I don't belong here, or the final programming assignment was absurd.

By Adrián J A R

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Feb 4, 2024

the course has good features but I think it lacks basic theory if you are not familiar already with linear algebra concepts. If that's the case I recommend better jon krohn free algebra courses instead of this