Python for probability, statistics, and machine learning [electronic resource] / José Unpingco.
2016
QA76.73.P98
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Online Access
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Can lend chapters, not whole ebooks
Details
Title
Python for probability, statistics, and machine learning [electronic resource] / José Unpingco.
Author
Unpingco, José, author.
ISBN
9783319307176 (electronic book)
3319307177 (electronic book)
9783319307152
3319307177 (electronic book)
9783319307152
Published
Switzerland : Springer International Publishing, 2016.
Language
English
Description
1 online resource : illustration
Item Number
10.1007/978-3-319-30717-6 doi
Call Number
QA76.73.P98
Dewey Decimal Classification
005.133
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 Scikit-learn 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 book is suitable for anyone with an undergraduate-level 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 open-source 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Available in Other Form
Print version: 9783319307152
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Table of Contents
Getting Started with Scientific Python
Probability
Statistics
Machine Learning
Notation.
Probability
Statistics
Machine Learning
Notation.