Practical Linear Algebra for Data Science
From Core Concepts to Applications Using Python
O'Reilly Media · Print & ebook · October 11, 2022
If you want to work in any computational or technical field, you need to understand linear algebra.
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- Who it’s for
- Linear algebra concepts in Python, from matrix operations to decompositions and practical applications.
- Themes
- Linear algebra with Python, Data science and machine learning, Applied matrix methods
- Genres
- Linear Algebra, Machine Learning, Python Programming
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Start with this bookAbout this book
If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications.
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This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: - The interpretations and applications of vectors and matrices - Matrix arithmetic (various multiplications and transformations) - Independence, rank, and inverses - Important decompositions used in applied linear algebra (including LU and QR) - Eigendecomposition and singular value decomposition - Applications including least-squares model fitting and principal components analysis
Matrices in Python
Linear algebra concepts in Python, from matrix operations to decompositions and practical applications.
Come here for
- Vectors, matrices, transformations, rank, and inverses in computational use.
- LU, QR, eigendecomposition, and singular value decomposition.
Expect
- Least-squares model fitting.
- Principal components analysis.
Where it sits
| Linear Algebra | 68% |
|---|---|
| Python Programming | 46% |
| Machine Learning | 35% |
Percentages are BookFrontier’s reading of the book.
Publisher categories
- Data Science
- Machine Learning
- Linear Algebra
- Authors
- Mike Cohen
- Publisher
- O'Reilly Media
- Published
- October 11, 2022
- Format
- Print & ebook
- Theme
- Linear algebra with Python · Data science and machine learning · Applied matrix methods
- Reading lane
- Data Science