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Practical Linear Algebra for Data Science
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.
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
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
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
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
372 pages, Kindle Edition
First published September 1, 2022
50 people are currently reading
129 people want to read
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Displaying 1 - 2 of 2 reviews
June 13, 2024
Really good book on the fundamentals of linear algebra through the framework of python. I really thought it was really good at explaining the concepts for anyone just starting out there linear algebra journey and appreciate the aspect of how this affects machine learning from the linear algebra perspective.
May 2, 2026
This book is a good bridge between the mathematical aspects of linear algebra and its applications in data science. It does not make you learn the concepts by forcing you to solve maths problems by hand. Instead, it focuses on an intuitive understanding of the concepts through visualizations.
The book covers all the basic linear algebra concepts needed for data science applications. Every concept is accompanied by a sample code or a coding exercise. If you are a beginner to programming, I recommend reading Chapter 16 first. It briefly goes into Python programming. Even if you are an experienced programmer, the chapter is worth reading first, as it introduces the style the author uses throughout the book.
The book covers all the basic linear algebra concepts needed for data science applications. Every concept is accompanied by a sample code or a coding exercise. If you are a beginner to programming, I recommend reading Chapter 16 first. It briefly goes into Python programming. Even if you are an experienced programmer, the chapter is worth reading first, as it introduces the style the author uses throughout the book.
Displaying 1 - 2 of 2 reviews