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Full Description
Linear Algebra for Data Science with Python provides an introduction to vectors and matrices within the context of data science. This book starts from the fundamentals of vectors and how vectors are used to model data, builds up to matrices and their operations, and then considers applications of matrices and vectors to data fitting, transforming time-series data into the frequency domain, and dimensionality reduction. This book uses a computational-first approach: the reader will learn how to use Python and the associated data-science libraries to work with and visualize vectors and matrices and their operations, as well as to import data to apply these techniques. Readers learn the basics of performing vector and matrix operations by hand but are also shown how to use several different Python libraries for performing these operations.
Key Features:
Teaches the most important concepts and techniques for working with multi-dimensional data using vectors and matrices.
Introduces readers to the some of the most important Python libraries for working with data, including NumPy and and PyTorch.
Examples using real data and engineering applications show the utility of the techniques covered in this book.
Includes many color visualizations to illustrate mathematical operations involving vectors and matrices.
Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material.
Contents
1. Introduction. 2. Vectors and Vector Operation. 3. Matrices and Operations. 4. Solving Systems of Linear Equations. 5. Exact and Approximate Data Fitting. 6. Transforming Data.
