Essential Math For Data Science Filetyoe Pdf


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Mathematics for Machine Learning


Mathematics for Machine Learning

Author: Marc Peter Deisenroth

language: en

Publisher: Cambridge University Press

Release Date: 2020-04-23


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Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Essential Math for Data Science


Essential Math for Data Science

Author: Thomas Nield

language: en

Publisher: "O'Reilly Media, Inc."

Release Date: 2022-05-26


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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

Foundations of Data Science


Foundations of Data Science

Author: Avrim Blum

language: en

Publisher: Cambridge University Press

Release Date: 2020-01-23


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Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.