Master the fundamentals of linear algebra and be prepared for courses in data science, engineering and more. Concise videos, problem sessions, exercises, quizzes, final exam, in an easy-to-use interface.
Summary
Get a comprehensive introduction to fundamental concepts in linear algebra with these video lessons and interactive notebooks. Follow along with the examples in the Wolfram Cloud and use the material to prepare for courses in data science, engineering and other fields. The course starts with linear equations and matrices, followed by determinants and eigenvalues, and then moves on to inner products and the singular value decomposition. Application sessions are included to show uses of linear algebra in the real world. Exercises and quizzes are provided for self-paced assessment.
Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Solve linear systems of equations
Compute products and inverses of matrices
Use determinants to solve linear systems
Work with eigenvalues and eigenvectors
Calculate least-squares approximations
Apply the singular value decomposition
Summary
Get a comprehensive introduction to fundamental concepts in linear algebra with these video lessons and interactive notebooks. Follow along with the examples in the Wolfram Cloud and use the material to prepare for courses in data science, engineering and other fields. The course starts with linear equations and matrices, followed by determinants and eigenvalues, and then moves on to inner products and the singular value decomposition. Application sessions are included to show uses of linear algebra in the real world. Exercises and quizzes are provided for self-paced assessment.
Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One)
You'll Learn To
Solve linear systems of equations
Compute products and inverses of matrices
Use determinants to solve linear systems
Work with eigenvalues and eigenvectors
Calculate least-squares approximations
Apply the singular value decomposition
