國立高雄大學圖資館 |

Language: English

Back

Python for probability, statistics, ...

SpringerLink (Online service)

Python for probability, statistics, and machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Python for probability, statistics, and machine learningby Jose Unpingco.
Author:	Unpingco, Jose.
Published:	Cham :Springer International Publishing :2016.
Description:	xv, 276 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Python (Computer program language)
Online resource:	http://dx.doi.org/10.1007/978-3-319-30717-6
ISBN:	9783319307176$q(electronic bk.)

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$q(electronic bk.)

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

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Python for probability, statistics, and machine learning
LDR:02470nmm a2200313 a 4500 001 484023
003 DE-He213
005 20160920103003.0
006 m d
007 cr nn 008maaau
008 161012s2016 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 676049
245 1 0 $a Python for probability, statistics, and machine learning $h [electronic resource] / $c by Jose Unpingco.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
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 215247
650 0 $a Probabilities $x Data processing. $3 199028
650 0 $a Statistics $x Data processing. $3 183693
650 1 4 $a Engineering. $3 210888
650 2 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Appl.Mathematics/Computational Methods of Engineering. $3 273758
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
710 2 $a SpringerLink (Online service) $3 273601
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)