Python for probability, statistics, and machine learning
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The work Python for probability, statistics, and machine learning represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Python for probability, statistics, and machine learning
Resource Information
The work Python for probability, statistics, and machine learning represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Python for probability, statistics, and machine learning
 Statement of responsibility
 José Unpingco
 Subject

 Probabilities  Data processing
 Probabilities  Data processing
 Electronic books
 Communications engineering / telecommunications
 Maths for engineers
 COMPUTER SCIENCE/General
 Python (Computer program language)
 Statistics  Data processing
 Statistics  Data processing
 Data mining
 Probability & statistics
 Maths for computer scientists
 Python (Computer program language)
 Language
 eng
 Summary
 This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. 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. Modern Python modules like Pandas, Sympy, and Scikitlearn are applied to simulate and visualize important machine learning concepts like the bias/variance tradeoff, crossvalidation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduatelevel exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key opensource Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes
 Cataloging source
 N$T
 Dewey number
 005.133
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA76.73.P98
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
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