# Matrix Decomposition: Getting Started with Matrix Decomposition

Math    |    Intermediate
• 11 Videos | 1h 15m 7s
• Includes Assessment
Matrix decomposition refers to the process of expressing a matrix as the product of other matrices. These factorized matrices are a lot easier to work with than the original matrix, as they usually possess specific properties desirable in the contexts of various mathematical procedures. Use this course to learn how to use matrix decomposition. Explore precisely what matrices and vectors are and how they're used. Then, study various matrix operations, such as computing the transpose and the inverse of a matrix. Moving on, identify why matrices are great for expressing linear transformations of points in a coordinate space. Work with important transformations, such as shearing, reflection, and rotation. Implement the LU, QR, and Cholesky decompositions and examine their applicability and restrictions. Upon completion, you'll know when and how to implement various matrix decompositions.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course define what's meant by a vector, a vector notation, a matrix notation, and an ordered set notation outline how to enumerate properties of matrices and vectors recognize several types of matrix operations mathematically define matrix decomposition mathematically define QR and Cholesky decomposition
• install libraries in Python perform LU decomposition perform QR decomposition perform Cholesky decomposition summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Vectors and Notation
• 3.
Linear Transformations with Matrices
• 4.
Matrix Types
• 5.
Matrix Decomposition
• 6.
QR and Cholesky Decomposition
• 7.
Getting Set Up in Python
• 8.
Performing LU Decomposition in Python
• 9.
Performing QR Decomposition in Python
• 10.
Performing Cholesky Decomposition in Python
• 11.
Course Summary

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