Python for Probability, Statistics, and Machine Learning, 3rd Edition

Book Description

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. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with Programming Tips that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers.

Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

中文：

书名：概率、统计和机器学习的Python，第3版

本书使用数学和Python代码的新颖集成，说明了将概率，统计和机器学习联系起来的基本概念，因此读者不仅可以使用现代Python模块使用统计和机器学习模型，还可以了解它们的相对优势和劣势。为了清楚地将理论概念与实际实现联系起来，作者提供了许多已完成的示例以及编程技巧，这些技巧鼓励读者编写高质量的Python代码。整个文本 (包括所有图形和数值结果) 可以使用提供的Python代码进行再现，从而使读者能够通过在自己的计算机上尝试相同的代码来进行后续操作。

诸如Pandas，Sympy，Scikit-learn，Statsmodels，Scipy，Xarray，Tensorflow和Keras之类的现代Python模块用于实现和可视化重要的机器学习概念，例如偏差/方差权衡，交叉验证，可解释性和正则化。用具体的数值例子解释和说明许多抽象的数学思想，例如概率的收敛模式。本书适用于具有概率，统计或机器学习经验的本科生，并且具有Python编程的基本知识的任何人。