Published by O'Reilly Media (edition 1), 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
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Published by O'Reilly Media, Incorporated, 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
Language: English
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Published by O'Reilly Media, Inc, USA, 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
Language: English
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Published by O'Reilly Media 7/5/2022, 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
Language: English
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Add to basketPaperback or Softback. Condition: New. Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics 1.23. Book.
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Published by Oreilly & Associates Inc, 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
Language: English
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Published by O'reilly Media Jun 2022, 2022
ISBN 10: 1098102932 ISBN 13: 9781098102937
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware - 'Master the math needed to excel in data science, machine learning, and statistics. In this book, . 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'--Back cover.
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Add to basketCondition: New. Über den AutorThomas 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 i.
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Add to basketCondition: 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.
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First Edition
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