Python for probability, statistics, and machine learning /
First Statement of Responsibility
Jos�e Unpingco.
EDITION STATEMENT
Edition Statement
Third edition.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Cham, Switzerland :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
2022.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
509p.
Other Physical Details
illustrations (black and white, and color)
NOTES PERTAINING TO BINDING AND AVAILABILITY
Text of Note
Available to OhioLINK libraries.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references.
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
Introduction -- Part 1 Getting Started with Scientific Python -- Installation and Setup -- Numpy -- Matplotlib -- Ipython -- Jupyter Notebook -- Scipy -- Pandas -- Sympy -- Interfacing with Compiled Libraries -- Integrated Development Environments -- Quick Guide to Performance and Parallel Programming -- Other Resources -- Part 2 Probability -- Introduction -- Projection Methods -- Conditional Expectation as Projection -- Conditional Expectation and Mean Squared Error -- Worked Examples of Conditional Expectation and Mean Square Error Optimization -- Useful Distributions -- Information Entropy -- Moment Generating Functions -- Monte Carlo Sampling Methods -- Useful Inequalities -- Part 3 Statistics -- Python Modules for Statistics -- Types of Convergence -- Estimation Using Maximum Likelihood -- Hypothesis Testing and P-Values -- Confidence Intervals -- Linear Regression -- Maximum A-Posteriori -- Robust Statistics -- Bootstrapping -- Gauss Markov -- Nonparametric Methods -- Survival Analysis -- Part 4 Machine Learning -- Introduction -- Python Machine Learning Modules -- Theory of Learning -- Decision Trees -- Boosting Trees -- Logistic Regression -- Generalized Linear Models -- Regularization -- Support Vector Machines -- Dimensionality Reduction -- Clustering -- Ensemble Methods -- Deep Learning -- Notation -- References -- Index.
0
SUMMARY OR ABSTRACT
Text of Note
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers. Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, interpretability, and regularization. Many abstract mathematical ideas, such as modes of convergence in probability, are explained and illustrated with concrete numerical examples. This book is suitable for anyone with undergraduate-level experience with probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Features a novel combination of modern Python implementations and underlying mathematics to illustrate and visualize the foundational ideas of probability, statistics, and machine learning; Includes meticulously worked-out numerical examples, all reproducible using the Python code provided in the text, that compute and visualize statistical and machine learning models thus enabling the reader to not only implement these models but understand their inherent trade-offs; Utilizes modern Python modules such as Statsmodels, Tensorflow, Keras, Sympy, and Scikit-learn, along with embedded "Programming Tips" to encourage readers to develop quality Python codes that implement and illustrate practical concepts.
OTHER EDITION IN ANOTHER MEDIUM
Author
Unpingco, Jos�e, 1969-
Place of Publication
Cham : Springer, 2022
Edition Statement
Third edition.
Title
Python for probability, statistics, and machine learning.
International Standard Book Number
9783031046476
Bibliographic Record Identifier
(OCoLC)1328003998.
TOPICAL NAME USED AS SUBJECT
Entry Element
Python (Computer program language)
Entry Element
Probabilities
Entry Element
Statistics
Entry Element
Machine learning.
Topical Subdivision
Data processing.
Topical Subdivision
Data processing.
(SUBJECT CATEGORY (Provisional
Subject Category Subdivision Code
TJK
Subject Category Subdivision Code
TEC041000
Subject Category Subdivision Code
TJK
System Code
bicssc
System Code
bisacsh
System Code
thema
DEWEY DECIMAL CLASSIFICATION
Number
005
.
13/3
Edition
23/eng/20221117
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
73
Book number
.
P98U4
2022
PERSONAL NAME - PRIMARY RESPONSIBILITY
Entry Element
Unpingco, Jos�e,
Dates
1969-
CORPORATE BODY NAME - SECONDARY RESPONSIBILITY
Entry Element
Ohio Library and Information Network.
ORIGINATING SOURCE
Agency
کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه
Date of Transaction
20231014060308.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
Python for Probability, Statistics, and Machine Learning-Springer (2022).pdf