Python for probability, statistics, and machine learning
By: Unpingco, Jose.
Material type:
BookPublisher: Cham : Springer, 2022Edition: 3rd ed.Description: xii, 509 p. ; ill., (some col.), 25 cm.ISBN: 9783031046476.Subject(s): Statistics Data processing | Data mining | Discrete mathematics | Telecommunications | Bernstein von-Mises theorem | Central limit theorem | Delta Method | Fisher Ecact Test | Generalized Linear Models | Hazard functions | Inverse CDF Method | Jupyter notebook | Kernel trick | Logilinear models | Mann-Whitney -Wilcoxen Test | Neyman-Pearson test | Plug-in principle | Rejection Method | Uniqueness theorem | Wald TestDDC classification: 006.31 Summary: Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Books | 006.31 UNP (Browse shelf) | Available | 034155 |
Includes bibliographical references and index.
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.
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