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
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.
This is a great course to built foundation for Machine Learning. Both the lecturers are amazing and great use of technology in presenting the concepts. Great example linked to PageRank algorithm.
Professors teaches in so much friendly manner. This is beginner level course. Don't expect you will dive deep inside the Linear Algebra. But the foundation will become solid if you attend this course.
The content of the course is very relevant, and the instructors are really fun and helpful.My only suggestion is to upload revisions for each assessment, so we can understand what we are doing wrong.
About the Mathematics for Machine Learning Specialization
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