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Python for probability, statistics, ...

Unpingco, Jose.

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Python for probability, statistics, and machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Python for probability, statistics, and machine learning/ by Jose Unpingco.
Author:	Unpingco, Jose.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xv, 276 p. :ill., digital ;24 cm.
[NT 15003449]:	Getting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
Contained By:	Springer eBooks
Subject:	Python (Computer program language) -
Online resource:	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:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.133

Python for probability, statistics, and machine learning
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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.
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