Authors:
- 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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Table of contents (5 chapters)
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Front Matter
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Back Matter
About this book
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.
This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Reviews
“The book is aimed primarily at intermediate or advanced Python programmers … . this work is a generally sound and comprehensive overview of the areas it covers. We recommend it to Python programmers interested in growing in these areas or experts in these areas interested in learning how to deal with them in Python.” (Eugene Callahan and Yujia Zhang, Computing Reviews, October 15, 2020)
Authors and Affiliations
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San Diego, USA
José Unpingco
About the author
Bibliographic Information
Book Title: Python for Probability, Statistics, and Machine Learning
Authors: José Unpingco
DOI: https://doi.org/10.1007/978-3-030-18545-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-18547-3Published: 14 August 2020
eBook ISBN: 978-3-030-18545-9Published: 29 June 2019
Edition Number: 2
Number of Pages: XIV, 384
Number of Illustrations: 128 b/w illustrations, 37 illustrations in colour
Topics: Communications Engineering, Networks, Probability and Statistics in Computer Science, Mathematical and Computational Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Data Mining and Knowledge Discovery