大葉大學圖書館 |

Python for probability, statistics, and machine learning[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.133
書名/作者:	Python for probability, statistics, and machine learning/ by Jose Unpingco.
作者:	Unpingco, Jose.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xv, 276 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Python (Computer program language)
標題:	Probabilities - Data processing.
標題:	Statistics - Data processing.
標題:	Engineering.
標題:	Communications Engineering, Networks.
標題:	Appl.Mathematics/Computational Methods of Engineering.
標題:	Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
標題:	Probability and Statistics in Computer Science.
標題:	Data Mining and Knowledge Discovery.
ISBN:	9783319307176
ISBN:	9783319307152
內容註:	Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
摘要、提要註:	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. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes.
電子資源:	http://dx.doi.org/10.1007/978-3-319-30717-6

Python for probability, statistics, and machine learning[electronic resource] /
Unpingco, Jose.

Python for probability, statistics, and machine learning[electronic resource] /by Jose Unpingco. - Cham :Springer International Publishing :2016. - xv, 276 p. :ill., digital ;24 cm.

Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.

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. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes.

ISBN: 9783319307176

Standard No.: 10.1007/978-3-319-30717-6doiSubjects--Topical Terms:

339754
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Python for probability, statistics, and machine learning[electronic resource] /
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