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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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276 - 300 of 355 Reviews for Linear Algebra for Machine Learning and Data Science

By Thokachichu T

Jun 16, 2023

Opened my eyes about the concepts i've learned in my UG studies...which i've been wondering how to implement practically, especially in ML and DS : )

By jasser A

Jul 7, 2023

It's a great course with professional professors but there is one problem i hoped they made lab lessons videos but overall I loved it.

By Jathavan S

Jan 6, 2024

Good course, programming assignments were partially too easy. Portion around eigenvalues and eigenvectors appears to be a bit rushed.

By Douglas L

Oct 5, 2023

The course was excellent – clear videos with great content, and the problems were set at a good level, as were the programming labs.

By Emmanuel M M B

Jul 16, 2023

The eigenvalues and eigenvectors week is not clear and seems to have been rushed over. Otherwise the other weeks were clear.

By Putri R N M

Mar 19, 2024

I'm overwhelmed at coding sections, I'm soo confused that i started crying, but overall, that was great experience.

By Abraham O

Oct 23, 2023

It's hard but I loved it. I would have preferred the instructor did more labs or a video of them doing the labs.

By S S P (

May 27, 2023

Course syllabus and structure is great, but I felt like there was not enough practice problems and examples.

By Luisana F

Dec 27, 2023

Some concepts were a little complex to understand however, I did learn a lot and I'm very grateful for that

By Hajji H

May 31, 2023

great course, it really does a great job of getting you familiar with important topics in linear algebra.

By deidelma 0

Sep 27, 2023

Good quality teaching, but the lack of enough worked examples detracts a bit from the educational value.

By mohammad a j

Jul 25, 2023

in last course quize there was a question that it seems there was not any material about that question.

By Rohan J

Jul 27, 2023

The course is really Good with practical examples. The instructor also explains everything clearly

By Erfan G

Mar 11, 2023

the last part(eigen values and vectors) is very complicate, I guess it has to explain more

By Jim R

Mar 23, 2023

A very quick overview of linear algebra with useful explanation of how it applies to AI

By Rob T

Apr 26, 2024

Excellent explanations--I withdrew because I could not get through the Python labs

By Darko Š

Aug 12, 2023

Week 4 was a little bit fast or not overly explained, but overall very good course

By Victor M

Jun 13, 2023

Very interesting course, but week four needs to re-done with better explanations

By Ramez D

Jun 2, 2023

i had some issues with the 4th weeek as some concepts was not explained clearly

By Leonardo E

Apr 24, 2023

personally, videos about coding itself and not just math would make it 100/100

By 李恒毅

Aug 23, 2023

I hope this course can go into more depth and provide more practice exercises

By Moro A W

Aug 18, 2023

I really liked how the topis were presented and how each topic was explain.

By marzieh m

Sep 13, 2023

step by step and mostly easy to learn. I took one star for the last week.

By Ahmed S K

May 17, 2023

It was a good course. parts of the explanation was a bit rushed.

By Ilya Z

Jan 2, 2024

There was a problem compiling the code from my Jupiter notebook