#### Computational Linear Algebra With Python & Numpy

13 May 2024

Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.27 GB | Duration: 3h 24m

Linear algebra computations using NumPy & SciPy, matrix operations, linear decomposition, principal component analysis
What you'll learn
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
Requirements
No previous experience in linear algebra is required
Basic knowledge in Python and Numpy
Description
Overview
Section 1: Introduction
Lecture 1 Introduction to the Course
Lecture 3 Whom This Course is Intended for?
Section 2: Tools & Resources
Lecture 4 Tools & Resources
Section 3: Introduction to Linear Algebra
Lecture 5 Introduction to Linear Algebra
Section 4: Scalar, Vector, Matrix, and Tensor
Lecture 6 Scalar, Vector, Matrix, and Tensor
Section 5: Matrix Addition & Subtraction with Numpy
Lecture 7 Matrix Addition & Subtraction with Numpy
Section 6: Matrix Multiplications with Numpy
Lecture 8 Matrix Multiplications with Numpy
Section 7: Matrix Inverse & Transpose with Numpy
Lecture 9 Matrix Inverse & Transpose with Numpy
Section 8: Calculating Matrix Determinant with Numpy
Lecture 10 Calculating Matrix Determinant with Numpy
Section 9: Calculating Matrix Norm, Trace, and Rank with Numpy
Lecture 11 Calculating Matrix Norm, Trace, and Rank with Numpy
Section 10: Solving System of Linear Equation Using Numpy
Lecture 12 Solving System of Linear Equation Using Numpy
Section 11: Calculating Eigenvalues & Eigenvectors with Numpy
Lecture 13 Calculating Eigenvalues & Eigenvectors with Numpy
Section 12: LU, QR, and Cholesky Decomposition
Lecture 14 LU, QR, and Cholesky Decomposition
Section 13: Creating Tensor with Numpy
Lecture 15 Creating Tensor with Numpy
Section 14: Building Movie Recommendation Engine Using Linear Decomposition
Lecture 16 Building Movie Recommendation Engine Using Linear Decomposition
Section 15: Compressing Image Using Singular Value Decomposition
Lecture 17 Compressing Image Using Singular Value Decomposition
Section 16: Predicting Real Estate Market Using Linear Regression
Lecture 18 Predicting Real Estate Market Using Linear Regression
Section 17: Text Mining Using Non Negative Matrix Factorization
Lecture 19 Text Mining Using Non Negative Matrix Factorization
Section 18: Performing Dimensionality Reduction Using Principle Component Analysis
Lecture 20 Performing Dimensionality Reduction Using Principle Component Analysis
Section 19: Conclusion & Summary
Lecture 21 Conclusion & Summary
People who are interested in learning about linear algebra and its real world applications,People who are interested in data science and machine learning
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