# Matrix Decomposition: Getting Started with Matrix Decomposition

Math    |    Intermediate
• 11 videos | 1h 15m 7s
• Includes Assessment
Rating 4.3 of 15 users (15)
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

• 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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