Python for Probability, Statistics, and Machine Learning 3/e

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.

使用數學和Python程式碼的新穎整合，本書說明了連結機率、統計和機器學習的基本概念，讓讀者不僅能夠使用現代Python模組來應用統計和機器學習模型，還能夠了解它們的相對優勢和劣勢。為了清晰地將理論概念與實際實現相連接，作者提供了許多實例和程式設計提示，鼓勵讀者撰寫優質的Python程式碼。整本書的內容，包括所有圖表和數值結果，都可以使用提供的Python程式碼重現，因此讀者可以通過在自己的電腦上實驗相同的程式碼來跟隨學習。

使用Pandas、Sympy、Scikit-learn、Statsmodels、Scipy、Xarray、Tensorflow和Keras等現代Python模組來實現和視覺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。解釋了許多抽象的數學概念，例如概率收斂模式，並用具體的數值例子進行了說明。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。

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.

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，涉獵各種高級數據科學主題，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。Unpingco博士目前是加利福尼亞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的本科和研究生機器學習和數據科學學位課程的講師。