Cohen, Mike X
Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python
- ISBN 13:
- 9781098120610
- author:
- Cohen, Mike X
- format:
- Paperback
- publisher:
- O'Reilly Media
- language:
- English
- Publication Year:
- 2022
- Pages:
- 300
- Dimensions:
- 23.1 x 17.5 x 1.8 centimetres (0
- Genre:
- Science, Mathematics, Algebra,
- Condition:
- New
- Availability:
- Item usually sent within 7 working days
Description
Linear algebra is a fundamental tool for working in computational and technical fields. This practical guide teaches core concepts as implemented in Python, covering topics such as vectors and matrices, matrix arithmetic, independence, rank, and inverses. You'll learn how to apply these concepts to real-world applications in data science, machine learning, deep learning, and more. With this book, you'll gain a deeper understanding of modern analysis methods and algorithms, including eigendecomposition, singular value decomposition, and least-squares model fitting. The author provides a clear introduction to the key concepts and techniques used in applied linear algebra, making it an ideal resource for practitioners and students working with computer technology and algorithms. By mastering these core concepts, you'll be able to implement and adapt various modern analysis methods and algorithms, enhancing your skills in data science, machine learning, and other technical fields.
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