Python for Probability, Statistics, and Machine Learning

Unpingco, José

Python for Probability, Statistics, and Machine Learning

商品描述

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.

商品描述(中文翻譯)

本書通過數學和Python代碼的創新結合，展示了連接概率、統計和機器學習的基本概念，讓讀者不僅可以使用現代Python模塊應用統計和機器學習模型，還能理解它們的相對優勢和劣勢。為了將理論概念清晰地與實際實現相連接，作者提供了許多實例和“編程提示”，鼓勵讀者編寫優質的Python代碼。整個文本，包括所有圖表和數值結果，都可以使用提供的Python代碼重現，從而使讀者能夠通過在自己的計算機上使用相同的代碼進行實驗跟隨學習。

本書使用了現代Python模塊，如Pandas、Sympy、Scikit-learn、Statsmodels、Scipy、Xarray、Tensorflow和Keras，來實現和可視化重要的機器學習概念，如偏差/方差折衷、交叉驗證、可解釋性和正則化。書中解釋了許多抽象的數學概念，例如概率收斂模式，並通過具體的數值示例進行了說明和演示。本書適合具有本科水平的概率、統計或機器學習經驗，並具備基本的Python編程知識的任何人。

作者簡介

Dr. José Unpingco completed his PhD from the University of California (UCSD), San Diego and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data science topics, with deep experience in machine learning. He was the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD) where he also spearheaded the DoD-wide adoption of scientific Python. In his time as the primary scientific Python instructor for the DoD, he taught over 600 scientists and engineers. Dr. Unpingco is currently the Vice President for Machine Learning/Data Science for the Gary and Mary West Health Institute, a non-profit Medical Research Organization in San Diego, California. He is also a lecturer at UCSD for their undergraduate and graduate Machine Learning and Data Science degree programs.


作者簡介(中文翻譯)

Dr. José Unpingco在加州大學聖地亞哥分校（UCSD）獲得博士學位，並在工業界擔任工程師、顧問和教師，涉獵廣泛的高級數據科學主題，尤其在機器學習方面具有豐富經驗。他曾擔任國防部（DoD）大規模信號和圖像處理的現場技術主管，並推動了DoD在科學Python的廣泛應用。在擔任DoD的主要科學Python教師期間，他教授了超過600名科學家和工程師。目前，Unpingco博士是位於加州聖地亞哥的非營利醫學研究機構Gary and Mary West Health Institute的機器學習/數據科學副總裁。他還是UCSD的講師，負責本科和研究生的機器學習和數據科學學位課程。

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