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Linear Algebra for Machine Learning and Data Science に戻る

DeepLearning.AI による Linear Algebra for Machine Learning and Data Science の受講者のレビューおよびフィードバック



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 struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held 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....




Very helpful and initiative course for who has a good mathematics background or not.



Great course and Great instructor. Happy to be enrolled


Linear Algebra for Machine Learning and Data Science: 1 - 10 / 10 レビュー

by Simone S


Too basic and chaotic

by Ahmed M G


Very helpful and initiative course for who has a good mathematics background or not.

by Mohamed A A E


Great course and Great instructor. Happy to be enrolled

by Fadhel H


everything was very great and wonderful for the material except for the eigenvalues and eigenvectors, it feels so off and lacking in details, luckily i'm an engineering student who had studied about linear algebra before so i was able to follow trough. well if you are a new comer for this field, i think you should prepare more for the eigenvalues and eigenvector materials

by João S


I missed some important concepts like Vector Projection and Matrix Calculus, just to exemplify some. I hope the other courses cover them.

by Samuel H


Excellent intro to the fascinating world of Linear Algebra.

by Ildana R



1. Concepts are explained in simple terms and complexity builds slowly over time. I felt more confident after each video rather than confused like with other courses.

2. Each video has small quizzes to solidify your understanding of small topics on the spot

3. Solving weekly quiz questions with pen and paper helps build mechanical memory.

Things that could be improved:

1. It would be nice to have a pdf file with all formulas in one place to refer to.

2. I wish there were a bit more examples of eigenvalues and eigenvectors, I had to do external research to fully understand the topic.

Overall, great course for beginners and those who have already started learning ML and want to get better intuition of math behind it.

by Aliaksei P


Great job!

by Jangsea P


This is too light.

by Farzad F


They teach banana and they ask to solve assignment with python

Do not waste your time

They already wasted my time for 2 weeks

So bad course