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Python for probability, statistics, and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Python for probability, statistics, and machine learningby Jose Unpingco.
作者:	Unpingco, Jose.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xvii, 509 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Python (Computer program language)
電子資源:	https://doi.org/10.1007/978-3-031-04648-3
ISBN:	9783031046483$q(electronic bk.)

Python for probability, statistics, and machine learning
Unpingco, Jose.

Python for probability, statistics, and machine learning[electronic resource] /by Jose Unpingco. - Third edition. - Cham :Springer International Publishing :2022. - xvii, 509 p. :ill., digital ;24 cm.

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.

ISBN: 9783031046483$q(electronic bk.)

Standard No.: 10.1007/978-3-031-04648-3doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / U56 2022

Dewey Class. No.: 005.133

Python for probability, statistics, and machine learning
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505 0 $a 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.
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