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Linear Algebra for Data Science with Python
Linear Algebra for Data Science with Python
1. Introduction
1.1. Who is this book for?
1.2. Why learn linear algebra from this book?
1.3. Brief Introduction to Data Science Terminology
1.4. What topics from linear algebra does this book cover?
1.5. What topics from linear algebra does this book
not
cover?
1.6. Extremely Brief Intro to Jupyter and Python
1.7. Chapter Summary
2. Vectors and Vector Operations
2.1. Introduction to Vectors
2.2. Visualizing Vectors
2.3. Applications
2.4. Special Vectors
2.5. Vector Operations
2.6. Vector Correlation and Projection
2.7. Chapter Summary
3. Matrices and Operations
3.1. Introduction to Matrices and Tensors
3.2. Matrix Operations
3.3. Matrix-Vector Multiplication as a Linear Transformation
3.4. Matrix Multiplication
3.5. Matrix Determinant and Linear Transformations
3.6. Eigenvalues and Eigenvectors
3.7. Chapter Summary
4. Solving Systems of Linear Equations
4.1. Working with Systems of Linear Equations Using Matrices and Vectors — Part 1
4.2. Working with Systems of Linear Equations Using Matrices and Vectors — Part 2
4.3. Matrix Inverses and Solving Systems of Linear Equations
4.4. Application to Eigenvalues and Eigenvectors
4.5. Approximate Solutions to Inconsistent Systems of Linear Equations
4.6. Chapter Summary
5. Exact and Approximate Data Fitting
5.1. Exact Data Fitting with Polynomials
5.2. Approximate Data Fitting
5.3. Chapter Summary
6. Transforming Data
6.1. Representing a Vector Using Projections: Spanning Sets and Bases
6.2. Universal Bases and the Discrete Fourier Transform
6.3. Set-Specific Bases: The Gram-Schmidt Algorithm
6.4. Alternative Bases via Eigendecomposition
6.5. Chapter Summary
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Index
A
|
J
|
M
|
V
A
array
J
Jupyter
LaTeX
M
matrix
V
vector