國立虎尾科技大學 |

Python for probability, statistics, and machine learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/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) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-30717-6
ISBN:	9783319307176

Python for probability, statistics, and machine learning
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:

566246
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Python for probability, statistics, and machine learning
LDR:02470nam a2200313 a 4500 001 863137
003 DE-He213
005 20160920103003.0
006 m d
007 cr nn 008maaau
008 170720s2016 gw s 0 eng d
020 $a 9783319307176 $q (electronic bk.)
020 $a 9783319307152 $q (paper)
024 7 $a 10.1007/978-3-319-30717-6 $2 doi
035 $a 978-3-319-30717-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b U58 2016
100 1 $a Unpingco, Jose. $3 1021098
245 1 0 $a Python for probability, statistics, and machine learning $h [electronic resource] / $c by Jose Unpingco.
260 $a Cham : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
300 $a xv, 276 p. : $b ill., digital ; $c 24 cm.
505 0 $a Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
520 $a 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.
650 0 $a Python (Computer program language) $3 566246
650 0 $a Probabilities $x Data processing. $3 678601
650 0 $a Statistics $x Data processing. $3 672142
650 1 4 $a Engineering. $3 561152
650 2 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Appl.Mathematics/Computational Methods of Engineering. $3 669335
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 782247
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-30717-6
950 $a Engineering (Springer-11647)