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 stars74.66%
- 4 stars19.78%
- 3 stars3.39%
- 2 stars1.14%
- 1 star1%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA
Efficient, targeted course for learning the language and basic operations within linear algebra. Excellent for those working full-time, and for those without much experience with linear algebra.
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.
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.
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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