Overview
- 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
- Includes supplementary material: sn.pub/extras
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Table of contents (5 chapters)
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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.
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Bibliographic Information
Book Title: Python for Probability, Statistics, and Machine Learning
Authors: José Unpingco
DOI: https://doi.org/10.1007/978-3-319-30717-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
eBook ISBN: 978-3-319-30717-6Published: 16 March 2016
Edition Number: 1
Number of Pages: XV, 276
Number of Illustrations: 214 b/w illustrations, 7 illustrations in colour
Topics: Communications Engineering, Networks, Mathematical and Computational Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Probability and Statistics in Computer Science, Data Mining and Knowledge Discovery