عنوان

Python for Probability, Statistics, and Machine Learning /

شابک

3030185451

شابک

9783030185459

شابک اشتباه

3030185443

شابک اشتباه

9783030185442

شابک اشتباه

9783030185459

عنوان اصلي

Python for Probability, Statistics, and Machine Learning /

نام عام مواد

[Book]

نام نخستين پديدآور

by José Unpingco.

وضعيت ويراست

2nd ed. 2019.

محل نشرو پخش و غیره

Cham :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

2019.

نام خاص و کميت اثر

1 online resource (XIV, 384 pages 164 illustrations, 37 illustrations in color.) :

ساير جزييات

online resource

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

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. 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. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras 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 updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

منبع سفارش / آدرس اشتراک

Springer

شماره استاندارد بين المللي کتاب و موسيقي

9783030185442

شماره استاندارد بين المللي کتاب و موسيقي

9783030185466

شماره استاندارد بين المللي کتاب و موسيقي

9783030185473

موضوع مستند نشده

Computer science.

موضوع مستند نشده

Data mining.

موضوع مستند نشده

Engineering mathematics.

موضوع مستند نشده

Statistics.

موضوع مستند نشده

Telecommunication.

موضوع مستند نشده

Probabilities-- Data processing.

موضوع مستند نشده

Python (Computer program language)

موضوع مستند نشده

Statistics-- Data processing.

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Unpingco, José,1969-

تاريخ عمليات

20200823090335.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y