عنوان

Python for probability, statistics, and machine learning /

ويژگيها

(electronic bk.)

ويژگيها

(electronic bk.)

شابک

9783031046483

شابک

303104648X

شابک اشتباه

9783031046476

شابک اشتباه

3031046471

شماره ISMN

10.1007/978-3-031-04648-3

شماره

15091

شماره کنترل سيستم

(OCoLC)1350913509

شماره کنترل سيستم غير معتبر يا حذف شده

(OCoLC)1350685678

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Python for probability, statistics, and machine learning /

نام نخستين پديدآور

Jos�e Unpingco.

وضعيت ويراست

Third edition.

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2022.

نام خاص و کميت اثر

509p.

ساير جزييات

illustrations (black and white, and color)

متن يادداشت

Available to OhioLINK libraries.

متن يادداشت

Includes bibliographical references.

متن يادداشت

Includes bibliographical references and index.

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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.

پديدآور

Unpingco, Jos�e, 1969-

محل نشر

Cham : Springer, 2022

وضعيت ويراست

Third edition.

عنوان

Python for probability, statistics, and machine learning.

شماره استاندارد بين المللي کتاب و موسيقي

9783031046476

شناسگر رکورد کتابشناختي

(OCoLC)1328003998.

عنصر شناسه ای

Python (Computer program language)

عنصر شناسه ای

Probabilities

عنصر شناسه ای

Statistics

عنصر شناسه ای

Machine learning.

تقسیم فرعی موضوعی

Data processing.

تقسیم فرعی موضوعی

Data processing.

کد مقوله موضوعی

TJK

کد مقوله موضوعی

TEC041000

کد مقوله موضوعی

TJK

کد سيستم

bicssc

کد سيستم

bisacsh

کد سيستم

thema

شماره

ويراست

شماره رده

نشانه اثر

عنصر شناسه اي

Unpingco, Jos�e,

تاريخ

1969-

عنصر شناسه اي

Ohio Library and Information Network.

سازمان

کتابخانه مرکزی و مرکز اطلاع رسانی دانشگاه

تاريخ عمليات

20231014060308.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

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

مشخصه منبع قراردادي

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

مشخصه منبع قراردادي

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

مشخصه منبع قراردادي

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

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource

يادداشت عمومي

Connect to resource (off-campus)

نوع ماده

BL

کد کاربرگه

270410

تكميل شده

Y