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

4.5
stars
1,318 ratings

About the Course

Newly updated for 2024! After completing this course, learners 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 Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. 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 plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career. This is a beginner-friendly program, with a recommended background of at least high school mathematics (functions, basic algebra). We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization....

Top reviews

NA

Jun 17, 2023

Very visual and application oriented and gives the context for machine learning and where linAL is applied in PCA and neural networks. The structure is really byte sized and fun to work with.

SP

Jul 26, 2023

This course is truly exceptional for individuals eager to strengthen their grasp of Linear Algebra concepts, paving the way for a deeper understanding of machine learning and data science.

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301 - 325 of 355 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 Kareem W

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Oct 29, 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 11, 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 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 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 26, 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

By Rudy

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Oct 20, 2023

Not enough explanation when it came to eigenvectors/values/spaces. The first 3 weeks were very explanatory but for this being a beginner course I think longer videos with more explanation would be better.

By VAIBHAV P Z

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May 1, 2024

Need more tough problems in assignments to build deeper understanding. Visual explanations for linear transformations could have been better. Explanation of span and bases could have been better.

By Amin

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Jun 13, 2023

The videos are easy to understand and really good. But I didn't like the graded assignments, they were hard to understand and sometimes there wasn't enough explanation.