This course provides basic concepts in Linear Algebra that are mainly fundamental to Machine Learning and Data Science. The course addresses Vector and Vector spaces, Matrix and determinant, Diagonalization of matrices, Factorization of matrices, SVD of matrices and its relation to PCA applications, Linear Transformations and Eigenvalues and Eigenvectors of matrices, System of linear equations and approximate solution techniques, Least Square and minimum norm solutions, Tensor Analysis and related applications in Data Science and ML.