Book Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics PDF Download - Thomas Nield
Download ebook ➡ http://ebooksharez.info/pl/book/638587/1003
Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics
Thomas Nield
Page: 347
Format: pdf, ePub, mobi, fb2
ISBN: 9781098102937
Publisher: O'Reilly Media, Incorporated
Download or Read Online Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Free Book (PDF ePub Mobi) by Thomas Nield
Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield PDF, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Epub, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Read Online, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Audiobook, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield VK, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Kindle, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Epub VK, Essential Math for Data Science: Take Control of Your Data with Fundamental Linear Algebra, Probability, and Statistics Thomas Nield Free Download
Overview
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