Applications of Linear Algebra Professional Certificate

What you will learn:
- Model and solve real-world problems using Markov chains, determinants, dynamical systems, and Google Page Rank.
- Construct the singular value decomposition (SVD) of a matrix and apply the SVD to estimate the rank and condition number of a matrix, construct a basis for the four fundamental spaces of a matrix, and construct a spectral decomposition of a matrix.
- Apply the iterative Gram Schmidt Process and the QR decomposition to construct an orthogonal basis of a subspace.
- Apply least-squares and multiple regression to construct a linear model from a data set.
- Apply eigenvalues and eigenvectors to solve optimization problems that are subject to distance and orthogonality constraints.

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Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD (edX)

This course takes you through roughly five weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. In the first part of this course you will explore methods to compute an approximate solution to an inconsistent system of equations that [...]
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Linear Algebra III: Determinants and Eigenvalues (edX)

This course takes you through roughly three weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology. At the beginning of this course we introduce the determinant, which yields two important concepts that you will use in this course.
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