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

4.6
stars
1,880 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...
...

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 471 Reviews for Linear Algebra for Machine Learning and Data Science

By Sanket M

•

Feb 24, 2025

Great

By Mahesh K

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Jan 10, 2025

Great

By Susi S M

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Mar 21, 2024

Great

By Ramy I A

•

Mar 7, 2024

thanx

By Michael C

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Dec 19, 2023

Great

By Stephen C

•

Dec 9, 2023

10/10

By RIPALDO L B

•

Sep 27, 2023

Keren

By Shahid R

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Jun 27, 2023

great

By Rini W S

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Nov 20, 2024

good

By Fadhila A S

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Oct 14, 2024

Good

By Hanifa F P

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Oct 11, 2024

good

By Muhammad S I

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Oct 8, 2024

nice

By Sasindu C P

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Jun 4, 2024

nice

By Anggi P S

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Mar 23, 2024

good

By Syehan H S

•

Oct 17, 2023

good

By Adek P D

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Sep 30, 2023

nice

By Pramitha D

•

Sep 22, 2023

cool

By partheniac

•

Jun 1, 2023

good

By Dr. M S

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Mar 23, 2023

Nice

By Không P Q H

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Jan 25, 2025

ok

By Noah C

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Dec 26, 2023

:)

By Latifah N

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Sep 28, 2023

ye

By Dhwani K

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Oct 3, 2024

.

By Hugo M

•

Sep 27, 2023

Overall a good course - but there is room for improvement here. At the beginning of the course it was stated that high school algebra was sufficient. But I felt like too many videos were spent on solving simple linear equations - the course assumes the learner should know this. On the other hand the more complex topics like eigenvalues, eigenvectors and eigenbasis were covered in less than 10 minutes! Yes, less than 2 videos for the more challenging parts of the course. Then a fairly difficult quiz for techniques and concepts that were barely touched in the videos. People like me expect a paid course to be more self-contained. I also felt like other videos were rushed. The instructor brings up really interesting geometric properties of the dot product, cosines etc but it is just gone through way too fast, too fast to digest or appreciate it, draw other connections etc. There needs to be more connection to the ML/AI world. How is solving linear systems going to help me in day to day ML practise? If eigenvectors are relevant to PCA, then please make a video about that (the intro doesn't count). Markov matrices are brought up in the final section as a motivating example, but again, we need videos explaining this please! That stuff is interesting and we want to see applications. Now for the "positive" comments. I think Luis is a great teacher overall, and I really liked the way there were visualisations, both in videos and in other sections. Playing around with vectors you could move around was great. The quizzes were mostly good and made you think carefully and practise the concepts. The labs helped see the practical side of the concepts. The course covers good ground.

By sangramjit s

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Jun 3, 2024

This is a beautiful course on linear algebra focused on the applications in Machine Learning. Mathematical content is at an intermediate level, not a very advanced one. Lecture videos are of short duration and each video is focused on one topic. In-video quizzes are useful to keep the learners engaged with the course material. Interactive tools are awesome for visualizing some of the theoretical concepts taught in the course. Practice quizzes and graded quizzes have been prepared with a lot of care. Application examples have been wisely selected and nicely presented with sufficient explanation. Finally, the best part of the course is the programming lab. A novice in Python programming will benefit immensely from these labs and graded programming assignments to upgrade his or her programming skills in linear algebraic problem-solving. A few suggestions to improve the course material - a rectangular system of linear equations (the number of equations & number of unknowns are unequal) could be introduced. How the rank of the augmented matrix and system (co-efficient) matrix can conclude the questions regarding the existence and uniqueness of the solution of a system of linear equations could be mentioned in the 2nd week's course material. A formal definition of linear transformation could be mentioned in the 3rd week's-course material. The mathematical foundation of PCA could have been explained in more detail.