Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics

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. A...

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Bibliographic Details
Main Author: Nield, Thomas (Computer programmer), (Author)
Format: eBook
Language: English
Published: Sebastopol, CA : O'Reilly Media, Inc., 2022.
Edition: First edition.
Subjects:
Data mining > Mathematics.
Mathematics.
Machine learning > Mathematics.
elektronické knihy
electronic books
ISBN: 9781098102906
9781098102883
9781098102937
Physical Description: 1 online zdroj (xiv, 332 stran) : ilustrace

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Table of contents

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020 |a 9781098102906  |q (electronic book) 
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100 1 |a Nield, Thomas  |c (Computer programmer),  |e author. 
245 1 0 |a Essential math for data science :  |b take control of your data with fundamental linear algebra, probability, and statistics /  |c Thomas Nield. 
250 |a First edition. 
264 1 |a Sebastopol, CA :  |b O'Reilly Media, Inc.,  |c 2022. 
264 4 |c ©2022 
300 |a 1 online zdroj (xiv, 332 stran) :  |b ilustrace 
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500 |a Obsahuje index. 
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520 |a 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. 
588 |a Description based on online resource; title from digital title page (viewed on April 14, 2023). 
590 |a Added to collection customer.56279.3 
650 0 |a Data mining  |x Mathematics. 
650 0 |a Mathematics. 
650 0 |a Machine learning  |x Mathematics. 
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655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |z 1098102932  |z 9781098102937 
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