Python for Probability, Statistics, and Machine Learning (Record no. 59117)

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fixed length control field 03442nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-3-319-30717-6
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421112555.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160316s2016 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319307176
-- 978-3-319-30717-6
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
100 1# - AUTHOR NAME
Author Unpingco, Jos�e.
245 10 - TITLE STATEMENT
Title Python for Probability, Statistics, and Machine Learning
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2016.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 276 p. 110 illus., 7 illus. in color.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
520 ## - SUMMARY, ETC.
Summary, etc 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.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-30717-6
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Mathematical statistics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Statistics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Applied mathematics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering mathematics.
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-- Electrical engineering.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Appl.Mathematics/Computational Methods of Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Probability and Statistics in Computer Science.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
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-- ZDB-2-ENG

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