Python for Probability, Statistics, and Machine Learning

This book provides a comprehensive introduction to probability, statistics, and machine learning, combining mathematics and Python codes to illustrate key concepts and their practical applications. It offers worked-out examples and programming tips to encourage readers to write quality Python code. The text is reproducible, enabling readers to experiment with the same code on their computers. It is suitable for individuals with undergraduate-level experience in probability, statistics, or machine learning and basic knowledge of Python programming.

Format: Hardback
Length: 509 pages
Publication date: 07 July 2022
Publisher: Springer International Publishing AG

This comprehensive book delves into the intricate relationship between probability, statistics, and machine learning, offering a novel approach to understanding these subjects. By seamlessly integrating mathematics and Python codes, the author aims to empower readers with the ability to employ modern Python modules for statistical and machine learning models while also gaining a deep appreciation of their relative strengths and weaknesses. To facilitate a clear connection between theoretical concepts and practical implementations, the book provides numerous worked-out examples accompanied by valuable Programming Tips that encourage the development of high-quality Python code. Moreover, the entire text, including all figures and numerical results, is fully reproducible using the provided Python codes, allowing readers to follow along by experimenting with the same code on their own computers.

Modern Python modules such as Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are employed to implement and visualize essential machine learning concepts such as the bias/variance trade-off, cross-validation, interpretability, and regularization. Numerous abstract mathematical ideas, including modes of convergence in probability, are explained and illustrated with concrete numerical examples.

This book is designed for individuals with undergraduate-level experience in probability, statistics, or machine learning, as well as a basic understanding of Python programming. It serves as a valuable resource for students, researchers, and practitioners seeking to enhance their knowledge and skills in these fields. By leveraging the power of mathematics and Python, readers will gain a comprehensive understanding of the principles that underpin statistical and machine learning models, enabling them to make informed decisions based on data analysis.

Weight: 1038g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031046476
Edition number: 3rd ed. 2022