Python for probability, statistics, and machine learning
- 3rd ed.
- Cham : Springer, 2022
- xii, 509 p. ; ill., (some col.), 25 cm.
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.
9783031046476
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 Test