Python for Probability, Statistics, and Machine Learning (2016)

José Unpingco

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商品描述

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.

商品描述(中文翻譯)

本書涵蓋了連結機率、統計和機器學習的關鍵概念,並使用Python模組進行演示。整本書的內容,包括所有圖表和數值結果,都可以使用Python程式碼和相關的Jupyter/IPython筆記本進行重現,這些筆記本可以作為附加下載提供。作者通過使用多種分析方法和Python程式碼來開發機器學習的關鍵直覺,從而將理論概念與具體實現相連接。現代Python模組,如Pandas、Sympy和Scikit-learn,被應用於模擬和可視化重要的機器學習概念,如偏差/方差折衷、交叉驗證和正則化。許多抽象的數學概念,如概率論中的收斂,都通過數值示例進行了發展和說明。本書適合具有本科水平的概率、統計或機器學習基礎知識和基本Python編程知識的任何人。