Python for Probability, Statistics, and Machine Learning
Springer International Publishing (Verlag)
978-3-319-30715-2 (ISBN)
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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.
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.
Getting Started with Scientific Python.- Probability.- Statistics.- Machine Learning.- Notation.
"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)
Erscheinungsdatum | 08.10.2016 |
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Zusatzinfo | XV, 276 p. 110 illus., 7 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
Mathematik / Informatik ► Informatik ► Theorie / Studium | |
Mathematik / Informatik ► Mathematik | |
Technik ► Elektrotechnik / Energietechnik | |
Technik ► Nachrichtentechnik | |
Schlagworte | Appl.Mathematics/Computational Methods of Engineer • Communications Engineering, Networks • data mining and knowledge discovery • Engineering • IPython Notebooks • machine learning • probability and statistics • Probability and Statistics in Computer Science • Python Toolchain • Scientific Python • Statistics for Engineering, Physics, Computer Scie |
ISBN-10 | 3-319-30715-0 / 3319307150 |
ISBN-13 | 978-3-319-30715-2 / 9783319307152 |
Zustand | Neuware |
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