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 stars19.70%
- 3 stars3.40%
- 2 stars1.14%
- 1 star1.02%
TOP REVIEWS FROM MATHEMATICS FOR MACHINE LEARNING: LINEAR ALGEBRA
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.
This was a terrific course; the instructors' are passionate and knowledgeable about the course material, the assignments are engaging and relevant, and the length of the videos feels "just right".
The instruction was good throughout, but I would urge fellow students to take the time to work through the problems as suggested. Also, the eigen- stuff is quite tricky and can fool you. Be careful.
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.
About the Mathematics for Machine Learning Specialization
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