Machine learning and data science are the most popular topics of research nowadays. They are applied in all the areas of engineering and sciences. Various machine learning tools provide a data-driven solution to various real-life problems. Basic knowledge of linear algebra is necessary to develop new algorithms for machine learning and data science. In this course, you will learn about the mathematical concepts related to linear algebra, which include vector spaces, subspaces, linear span, basis, and dimension. It also covers linear transformation, rank and nullity of a linear transformation, eigenvalues, eigenvectors, and diagonalization of matrices. The concepts of singular value decomposition, inner product space, and norm of vectors and matrices further enrich the course contents.
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Linear Algebra Basics
Indian Institute of Technology RoorkeeAbout this Course
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Approx. 21 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Describe the vector spaces, vector subspaces, basis, and dimension of a vector space.
Explain the linear transformations defined on vector spaces and eigenvalues and eigenvector of a matrix, symmetric and skew-symmetric matrices.
Explain diagonalizable matrices, their applications and the inner product, and the norm of vectors and matrices.
Skills you will gain
- Data Science
- Machine Learning
- Mathematics
- Linear Algebra
- Machine Learning (ML) Algorithms
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Intermediate Level
Approx. 21 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
20 minutes to complete
Getting Started with the Course
20 minutes to complete
1 reading
3 hours to complete
Vector Space
3 hours to complete
6 videos (Total 54 min), 2 readings, 2 quizzes
5 hours to complete
Linear Transformations and Eigenvalues
5 hours to complete
6 videos (Total 44 min), 5 readings, 2 quizzes
4 hours to complete
Diagonalizable Matrices and Their Applications
4 hours to complete
6 videos (Total 52 min), 3 readings, 2 quizzes
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