عنوان

Python for probability, statistics, and machine learning /

Qualification

(electronic bk.)

Qualification

(electronic bk.)

(Number (ISBN

9783031046483

(Number (ISBN

303104648X

Erroneous ISBN

9783031046476

Erroneous ISBN

3031046471

(Number (ISMN

10.1007/978-3-031-04648-3

Number

15091

System Control Number

(OCoLC)1350913509

Cancelled or Invalid Control Number

(OCoLC)1350685678

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Python for probability, statistics, and machine learning /

First Statement of Responsibility

Jos�e Unpingco.

Edition Statement

Third edition.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

2022.

Specific Material Designation and Extent of Item

509p.

Other Physical Details

illustrations (black and white, and color)

Text of Note

Available to OhioLINK libraries.

Text of Note

Includes bibliographical references.

Text of Note

Includes bibliographical references and index.

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

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.

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.

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 Subdivision Code

TJK

Subject Category Subdivision Code

TEC041000

Subject Category Subdivision Code

TJK

System Code

bicssc

System Code

bisacsh

System Code

thema

Number

Edition

Class number

Book number

Entry Element

Unpingco, Jos�e,

Dates

1969-

Entry Element

Ohio Library and Information Network.

Agency

کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه

Date of Transaction

20231014060308.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

Python for Probability, Statistics, and Machine Learning-Springer (2022).pdf

Uniform Resource Identifier

https://rave.ohiolink.edu/ebooks/ebc2/9783031046483

Uniform Resource Identifier

https://link.springer.com/10.1007/978-3-031-04648-3

Uniform Resource Identifier

http://proxy.ohiolink.edu:9099/login?url=https://link.springer.com/10.1007/978-3-031-04648-3

Public note

Connect to resource

Public note

Connect to resource

Public note

Connect to resource (off-campus)

BL

270410

Y