Python用于概率、统计和机器学习（True PDF，EPUB）
This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
英文| PDF，EPUB | 2016 | 288页| ISBN:3319307150 | 12.05 MB本书涵盖了在这些领域使用Python模块将概率、统计和机器学习联系起来的关键思想。整个文本，包括所有的数字和数值结果，都可以使用Python代码及其相关的Jupyter/IPython笔记本进行复制，这些笔记本作为补充下载提供。作者通过使用多种分析方法和Python代码进行有意义的示例来开发机器学习中的关键直觉，从而将理论概念与具体实现联系起来。Pandas、Sympy和Scikit-learn等现代Python模块被应用于模拟和可视化重要的机器学习概念，如偏差/方差权衡、交叉验证和正则化。许多抽象的数学概念，如概率论中的收敛性，都得到了发展，并通过数值例子进行了说明。这本书适合任何对概率、统计或机器学习有本科水平的接触，并且对Python编程有基本了解的人。本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。
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