Python for probability, statistics, and machine learning [electronic resource] / José Unpingco.
Unpingco, José, author.
2016
QA76.73.P98
Available Online

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Title Python for probability, statistics, and machine learning [electronic resource] / José Unpingco.
Author Unpingco, José, author.
ISBN 9783319307176 (electronic book)
3319307177 (electronic book)
9783319307152
DOI https://doi.org/10.1007/978-3-319-30717-6
Published Switzerland : Springer International Publishing, 2016.
Language English
Description 1 online resource : illustration
Other Standard Identifiers 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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Getting Started with Scientific Python
Probability
Statistics
Machine Learning
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