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Python for Probability, Statistics, and Machine Learning

Authors: Unpingco, José

  • Features fully updated explanation on how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods
  • New edition features Python version 3.7 and 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
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Buy this book

eBook $54.99
price for USA in USD (gross)
  • The eBook version of this title will be available soon
  • Due: September 29, 2019
  • ISBN 978-3-030-18545-9
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $89.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: September 1, 2019
  • ISBN 978-3-030-18544-2
  • Free shipping for individuals worldwide
About this Textbook

This textbook, fully updated to feature Python version 3.7, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules. 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. The update features full coverage of Web-based scientific visualization with Bokeh Jupyter Hub; Fisher Exact, Cohen’s D and Rank-Sum Tests; Local Regression, Spline, and Additive Methods; and Survival Analysis, Stochastic Gradient Trees, and Neural Networks and Deep Learning. 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 classes in probability, statistics, or machine learning and requires only rudimentary knowledge of Python programming.

About the authors

Dr. José Unpingco completed his PhD at the University of California, San Diego in 1997 and has since worked in industry as an engineer, consultant, and instructor on a wide-variety of advanced data processing and analysis topics, with deep experience in machine learning and statistics. As the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD), he spearheaded the DoD-wide adoption of scientific Python. He also trained over 600 scientists and engineers to effectively utilize Python for a wide range of scientific topics -- from weather modeling to antenna analysis. Dr. Unpingco is the cofounder and Senior Director for Data Science at a non-profit Medical Research Organization in San Diego, California. He also teaches programming for data analysis at the University of California, San Diego for engineering undergraduate/graduate students. He is author of Python for Signal Processing (Springer 2014) and Python for Probability, Statistics, and Machine Learning (2016)

Buy this book

eBook $54.99
price for USA in USD (gross)
  • The eBook version of this title will be available soon
  • Due: September 29, 2019
  • ISBN 978-3-030-18545-9
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover $89.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at +1-800-777-4643, Latin America please contact us at +1-212-460-1500 (24 hours a day, 7 days a week).
  • Due: September 1, 2019
  • ISBN 978-3-030-18544-2
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Python for Probability, Statistics, and Machine Learning
Authors
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-18545-9
DOI
10.1007/978-3-030-18545-9
Hardcover ISBN
978-3-030-18544-2
Edition Number
2
Number of Pages
XIV, 384
Number of Illustrations
120 b/w illustrations, 38 illustrations in colour
Topics