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ISBN 10 : 1098102932 ISBN 13 : 9781098102937
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Ajouter au panierPaperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Ajouter au panierPaperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Edité par O'Reilly Media, Inc, USA, 2022
ISBN 10 : 1098102932 ISBN 13 : 9781098102937
Langue: anglais
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Ajouter au panierPaperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Ajouter au panierPaperback. Etat : New. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks.
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Ajouter au panierKartoniert / Broschiert. Etat : 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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Edité par O'Reilly Media, Sebastopol, 2022
ISBN 10 : 1098102932 ISBN 13 : 9781098102937
Langue: anglais
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Ajouter au panierPaperback. Etat : new. Paperback. To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus.Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to:Recognize the nuances and pitfalls of probability mathMaster statistics and hypothesis testing (and avoid common pitfalls)Discover practical applications of probability, statistics, calculus, and machine learningIntuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and addedPerform calculus derivatives and integrals completely from scratch in PythonApply what you've learned to machine learning, including linear regression, logistic regression, and neural networks To succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Edité par Oreilly & Associates Inc, 2022
ISBN 10 : 1098102932 ISBN 13 : 9781098102937
Langue: anglais
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Ajouter au panierPaperback. Etat : Brand New. 350 pages. 9.19x7.00x0.73 inches. In Stock.
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Edité par O'reilly Media Jun 2022, 2022
ISBN 10 : 1098102932 ISBN 13 : 9781098102937
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : 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.