Python for Probability, Statistics, and Machine Learning 3/e
暫譯: Python 機率、統計與機器學習 第3版
Unpingco, José
- 出版商: Springer
- 出版日期: 2022-11-05
- 售價: $3,860
- 貴賓價: 9.5 折 $3,667
- 語言: 英文
- 頁數: 526
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031046471
- ISBN-13: 9783031046476
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相關分類:
Python、程式語言、Machine Learning、機率統計學 Probability-and-statistics
海外代購書籍(需單獨結帳)
相關主題
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)
使用數學與 Python 代碼的新穎整合，本書闡述了連結機率、統計與機器學習的基本概念，使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，還能理解它們的相對優缺點。為了清楚地將理論概念與實際實現相連結，作者提供了許多詳細的範例，並附上編程提示，鼓勵讀者撰寫高品質的 Python 代碼。整個文本，包括所有圖形和數值結果，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電腦上實驗相同的代碼來跟隨學習。
本書使用現代 Python 模組，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/方差權衡、交叉驗證、可解釋性和正則化。許多抽象的數學概念，例如機率中的收斂模式，均以具體的數值範例進行解釋和說明。本書適合任何具備本科程度的機率、統計或機器學習經驗，以及具備基本 Python 編程知識的讀者。
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)
喬斯·昂平科博士（Dr. José Unpingco）於加州大學聖地牙哥分校（University of California, San Diego, UCSD）獲得博士學位，並自此在業界擔任工程師、顧問和講師，專注於各種先進的數據科學主題，並在機器學習方面擁有深厚的經驗。他曾擔任美國國防部（Department of Defense, DoD）大型信號與影像處理的現場技術總監，並主導了全國防部範圍內科學 Python 的採用。在擔任國防部主要的科學 Python 講師期間，他培訓了超過600名科學家和工程師。昂平科博士目前是加州聖地牙哥的加里與瑪麗·韋斯特健康研究所（Gary and Mary West Health Institute）機器學習/數據科學副總裁，該機構是一個非營利的醫學研究組織。他同時也是加州大學聖地牙哥分校的講師，教授本科及研究生的機器學習和數據科學學位課程。
