Python for Probability, Statistics, and Machine Learning (PDF)
(Sprache: Englisch)
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...
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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 machinelearning 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.
Autoren-Porträt von José Unpingco
Dr. José 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.
Bibliographische Angaben
- Autor: José Unpingco
- 2016, 1st ed. 2016, 276 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319307177
- ISBN-13: 9783319307176
- Erscheinungsdatum: 16.03.2016
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 7.22 MB
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Sprache:
Englisch
Pressezitat
“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)Kommentar zu "Python for Probability, Statistics, and Machine Learning"
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