Python for Probability, Statistics, and Machine Learning

Author

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. 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. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras 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 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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9783319307152

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Springer

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José Unpingco

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Review

The purpose of this book is to introduce scientific Python to those who have a prior knowledge of probability and statistics as well as basic Python. this is a very valuable reference for those wishing to use these methods in a Python environment. I would strongly recommend this book for the intended audience or as a reference work. All in all, I strongly recommend this book for those who want to use Python in this area. (David E. Booth, Technometrics, Vol. 59 (2), April, 2017)

--This text refers to an out of print or unavailable edition of this title.

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I would strongly recommend this book for the intended audience or as a reference work...the book could profitably be used for a lab in conjunction with the Mathematical Statistics course. (David E. Booth, Kent State University)

--This text refers to an out of print or unavailable edition of this title.

About the Author

Dr. Jos Unpingco completed his PhD from the University of California, San Diego in 1998 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 multiple machine learning technologies. He was the onsite technical director for large-scale Signal and Image Processing for the Department of Defense (DoD) where he also spearheaded the DoD-wide adoption of scientific Python. As the primary scientific Python instructor for the DoD, he has taught Python to over 600 scientists and engineers. Dr. Unpingco is currently the Technical Director for Data Science for a non-profit Medical Research Organization in San Diego, California.
--This text refers to an out of print or unavailable edition of this title.

From the Back Cover

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.

--This text refers to an out of print or unavailable edition of this title.

Python for Probability, Statistics, and Machine Learning

Python

Machine Learning

Statistics

José Unpingco

Springer

José Unpingco

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