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Learning Python and a Few More Things

  • Guy Lebanon
  • Mohamed El-Geish
Chapter

Abstract

Python is one of the most popular programming languages. It’s broadly used in programming web applications, writing scripts for automation, accessing data, processing text, data analysis, etc. Many software packages that are useful for data analysis (like NumPy, SciPy, and Pandas) and machine learning (scikit-learn, TensorFlow, Keras, and PyTorch) can be integrated within a Python application in a few lines of code. In this chapter, we explore the programming language in a similar approach to the one we took for C++ and Java. In addition, we explore tools and packages that help accelerate the development of data-driven application using Python.

References

  1. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.Google Scholar
  2. K. P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.Google Scholar
  3. Anil K. Jain and Richard C. Dubes. Algorithms for Clustering Data. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1988. ISBN 0-13-022278-X.zbMATHGoogle Scholar
  4. Leonard Kaufman and Peter J. Rousseeuw. Finding groups in data : an introduction to cluster analysis. Wiley series in probability and mathematical statistics. Wiley, New York, 1990. ISBN 0-471-87876-6. A Wiley-Interscience publication.Google Scholar
  5. M. Lutz. Programming Python. O’Reilly Media Inc., fourth edition, 2011.Google Scholar
  6. M. Lutz. Learning Python. O’Reilly Media Inc., fifth edition, 2013.Google Scholar
  7. W. McKinney. Python for data analysis. O’Reilly Media Inc., 2013.Google Scholar
  8. S. Bird, E. Klein, and E. Loper, editors. Natural Language Processing with Python. O’Reilly Media Inc., 2009.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guy Lebanon
    • 1
  • Mohamed El-Geish
    • 2
  1. 1.AmazonMenlo ParkUSA
  2. 2.VoiceraSanta ClaraUSA

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