9781032659169 - Linear Algebra for Data Science with Python (Chapman & Hall/CRC The Python Series) de Shea, John M. (28 resultados)

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Condición: New. 1st edition NO-PA16APR2015-KAP.

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HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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hardcover. Condición: New.

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Hardback. Condición: New. New copy - Usually dispatched within 4 working days.

Hardback. Condición: New. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website.

Condición: New. 2025. 1st Edition. hardcover. . . . . .

Hardback. Condición: New. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website.

Condición: NEW.

Condición: New. 2025. 1st Edition. hardcover. . . . . . Books ship from the US and Ireland.

Condición: New. John M. Shea, PhD is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, where he has taught classes on stochastic methods, data science, and wireless communications for over 25 years. He earned his P.

Hardcover. Condición: Brand New. 264 pages. 9.18x6.12x10.00 inches. In Stock.

Hardback. Condición: New. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website.

Hardback. Condición: New. 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 some of the most important Python libraries for working with data, including NumPy and PyTorch.Demonstrate the application of linear algebra in real data and engineering applications.Includes many color visualizations to illustrate mathematical operations involving vectors and matrices.Provides practice and feedback through a unique set of online, interactive tools on the accompanying website.

Hardcover. Condición: new. Hardcover. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: Brand New. 264 pages. 9.18x6.12x10.00 inches. In Stock. This item is printed on demand.

Hardcover. Condición: new. Hardcover. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations.

Hardcover. Condición: new. Hardcover. 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 matricesIntroduces readers to some of the most important Python libraries for working with data, including NumPy and PyTorchDemonstrate the application of linear algebra in real data and engineering applicationsIncludes many color visualizations to illustrate mathematical operations involving vectors and matricesProvides practice and feedback through a unique set of online, interactive tools on the accompanying website Learn how to use Python and associated data-science libraries to work with and visualize vectors and matrices and their operations, as well import data to apply these techniques. Learn basics of performing vector and matrix operations by hand, how to use several different Python libraries for performing operations. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. Linear Algebra for Data Science with Python | John M. Shea | Buch | Einband - fest (Hardcover) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032659169 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.