Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
Nield, Thomas
ISBN 10: 1098102932 ISBN 13: 9781098102937
Editorial: O'Reilly Media, 2022
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Encuadernación de tapa blanda
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Big River Books, Powder Springs, GA, Estados Unidos de America
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Vendedor de AbeBooks desde 7 de junio de 2013
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Descripción
Descripción:
This book is in good condition. The cover has minor creases or bends. The binding is tight and pages are intact. Some pages may have writing or highlighting. N° de ref. del artículo BRV.1098102932.G
Sinopsis:
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.
Learn how to:
- Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning
- Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon
- Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance
- Manipulate vectors and matrices and perform matrix decomposition
- Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks
- Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market
Acerca del autor: Thomas Nield is the founder of Nield Consulting Group as well as an instructor at O'Reilly Media and University of Southern California. He enjoys making technical content relatable and relevant to those unfamiliar or intimidated by it. Thomas regularly teaches classes on data analysis, machine learning, mathematical optimization, and practical artificial intelligence. He's authored two books, including Getting Started with SQL (O'Reilly) and Learning RxJava (Packt).
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Detalles bibliográficos
Título: Essential Math for Data Science: Take ...
Editorial: O'Reilly Media
Año de publicación: 2022
Encuadernación: Encuadernación de tapa blanda
Condición: good
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ISBN 13: 9781098102937
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Condición: Used - Very Good. Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career.Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market. Nº de ref. del artículo: CS-C4CF-D596
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