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Python for Probability, Statistics, and Machine Learning by Jose Unpingco (Hardcover, 2016)

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Product Information

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.

Product Identifiers

Publisher
Springer International Publishing Ag
ISBN-13
9783319307152
eBay Product ID (ePID)
221441938

Product Key Features

Author
Jose Unpingco
Publication Name
Python for Probability, Statistics, and Machine Learning
Format
Hardcover
Language
English
Subject
Engineering & Technology, Computer Science, Mathematics
Publication Year
2016
Type
Textbook
Number of Pages
276 Pages

Dimensions

Item Height
235mm
Item Width
155mm
Item Weight
720g

Additional Product Features

Title_Author
Jose Unpingco
Country/Region of Manufacture
Switzerland

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Python for Probability, Statistics, and Machine Learning by Jose Unpingco (Hardcover, 2016)

About this product

Product Information

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

Best Selling in Adult Learning & University

The Weed Forager's Handbook: A Guide to Edible and Medicinal Weeds in Australia by Adam Grubb, Annie Raser-Rowland (2012, Paperback)

Australian Native Bee Book: Keeping Stingless Bee Hives for Pets, Pollination and Sugarbag Honey by Tim Heard (Paperback, 2015)

The Falsification of History: Our Distorted Reality by John Hamer (Paperback, 2013)

Permaculture: A Designer's Manual by Bill Mollison (Hardcover, 1988)

In-Basket Case Studies by Guy Adamson (Paperback, 1986)

Empire of the Summer Moon: Quanah Parker and the Rise and Fall of the Comanches, the Most Powerful Indian Tribe in American History by S.C. Gwynne (Paperback, 2011)

Botanical Field Guide by Stefan Mager (Fold-out book or chart, 2021)

Save on Adult Learning & University

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The Red Book: Liber Novus by Carl Gustav Jung (English) Hardcover Book

Sally Fallon Morell Thomas S. Cowan The Contagion Myth (Hardback) (US IMPORT)

Steven Holzner Physics Essentials For Dummies (Paperback)

Havard's Nursing Guide to Drugs

T. Jared Bunch Matthew LaPlante John D. Day The AFib Cure (Paperback)

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