Linear algebra computations using NumPy & SciPy, matrix operations, linear decomposition, principal component analysis
Sub Category
- Math
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Objectives
- Learn the basic fundamentals of linear algebra, such as getting to know its real world applications and important key concepts
- Learn about the difference between scalar, vector, matrix, and tensor
- Learn how to add and subtract matrix using Numpy
- Learn how to multiply matrix using Numpy
- Learn how to inverse and transpose matrix using Numpy
- Learn how to calculate matrix determinant using Numpy
- Learn how to calculate matrix norm, trace, and rank using Numpy
- Learn how to solve system of linear equation using Numpy
- Learn how to calculate eigenvalues and eigenvectors using Numpy
- Learn about LU, QR, and Cholesky decomposition
- Learn how to create, slice, and reshape tensor using Numpy
- Learn how to build movie recommendation engine using linear decomposition
- Learn how to build image compressor using singular value decomposition
- Learn how to predict real estate market using linear regression
- Learn how to do text mining using non negative matrix factorization
- Learn how to perform dimensionality reduction using principal component analysis
Pre Requisites
- No previous experience in linear algebra is required
- Basic knowledge in Python and Numpy
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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