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.
This course is part of the Mathematics for Machine Learning Specialization
About this Course
Skills you will gain
- Eigenvalues And Eigenvectors
- Basis (Linear Algebra)
- Transformation Matrix
- Linear Algebra
Syllabus - What you will learn from this course
Introduction to Linear Algebra and to Mathematics for Machine Learning
Vectors are objects that move around space
Matrices in Linear Algebra: Objects that operate on Vectors
Matrices make linear mappings
- 5 stars74.72%
- 4 stars19.69%
- 3 stars3.40%
- 2 stars1.13%
- 1 star1.03%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA
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.
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.
This is the BEST course if anyone wants to learn linear algebra for machine learning. Lectures are clear and very understandable and quiz questions are great, too. Thank you for this great course.
Excellent review of Linear Algebra even for those who have taken it at school. Handwriting of the first instructor wasn't always legible, but wasn't too bad. Second instructor's handwriting is better.
About the Mathematics for Machine Learning Specialization
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