Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python

This book provides a practical guide to linear algebra as implemented in Python, covering its core concepts and applications in data science, machine learning, deep learning, computational simulations, and biomedical data processing. It is ideal for practitioners and students using computer technology and algorithms.

Format: Paperback / softback
Length: 300 pages
Publication date: 20 September 2022
Publisher: O'Reilly Media

The importance of linear algebra in computational and technical fields cannot be overstated. As the foundation of nearly all algorithms and analyses implemented in computers, linear algebra is a crucial discipline for anyone seeking to work in these fields. However, the way linear algebra is typically presented in decades-old textbooks differs significantly from the way it is used by professionals today to solve real-world modern applications.

This practical guide from Mike X Cohen aims to bridge this gap by teaching the core concepts of linear algebra as implemented in Python, a widely used programming language in data science, machine learning, deep learning, computational simulations, and biomedical data processing. By gaining knowledge from this book, readers will be able to understand, implement, and adapt a wide range of modern analysis methods and algorithms, making them well-equipped for practitioners and students working with computer technology and algorithms.

The book begins by introducing the interpretations and applications of vectors and matrices, which form the basis of linear algebra. It then delves into matrix arithmetic, covering various multiplications and transformations that are essential for understanding and manipulating linear systems. The concept of independence, rank, and inverses is explored, providing readers with the tools to analyze and solve linear equations and matrices.

Important decompositions such as the LU (Lower-Upper) and QR (Quadratic Residual) are introduced, which are used in applied linear algebra to solve complex systems of equations. Eigendecomposition and singular value decomposition are also discussed, as they are crucial for analyzing and extracting meaningful information from matrices.

The book showcases numerous applications of linear algebra in various fields, including least-squares model fitting, principal components analysis, and machine learning. Readers will learn how to use linear algebra to solve optimization problems, analyze data, and make predictions, making it an invaluable resource for anyone working in these areas.

In conclusion, linear algebra is a fundamental discipline that plays a crucial role in computational and technical fields. This practical guide from Mike X Cohen provides a comprehensive and up-to-date introduction to linear algebra as implemented in Python, making it an essential resource for practitioners and students seeking to excel in these fields. By gaining knowledge from this book, readers will be well-equipped to understand, implement, and adapt modern analysis methods and algorithms, enabling them to solve complex real-world problems effectively.

Dimension: 233 x 178 (mm)
ISBN-13: 9781098120610