Electronic Resource

Python for Probability, Statistics, and Machine Learning

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908001771	EB0002405	005.133 Unp p	Central Library	Available

Detail Information

Series Title

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Call Number

005.133 Unp p

Publisher

Switzerland : Springer Cham., 2019

Collation

XIV, 384p.:Ill

Language

English

ISBN/ISSN

978-3-030-18545-9

Classification

005.133

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Probabilities--Data processing

Specific Detail Info

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Statement of Responsibility

BRF

Other version/related

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