In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.
About this Course
Skills you will gain
- 5 stars74.70%
- 4 stars19.73%
- 3 stars3.40%
- 2 stars1.14%
- 1 star1%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA
even though my code was right in the last assignment the grader kept getting timed out. it took 3 days to work and in the end the code was same. the course on the other hand was quite good and easy.
Great content and direction. Only negative is the sometimes frustrating experience with the Jupyter Notebooks: debugging what has gone wrong is very difficult, due to a lack of good error messages.
Good course with nice lecturer.
Some topics should be explain more in detail and have some further reading / exercise for practicing.
For overall, this course is worth the time and money spend.
Amazing course, great instructors. The amount of working linear algebra knowledge you get from this single course is substantial. It has already helped solidify my learning in other ML and AI courses.
About the Mathematics for Machine Learning Specialization
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I subscribe to this Specialization?
Is financial aid available?
More questions? Visit the Learner Help Center.