Ementorhub

What you'll 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.

There are 6 modules in this course

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.

Vector Space

Linear Transformations and Eigenvalues

Diagonalizable Matrices and Their Applications

Singular Value Decomposition of a Matrix and Inner Product of Vectors

Term-End Assignment