A Tutorial on Machine Learning and Data Science Tools with Python

Marcus Daniel Bloice, Andreas Holzinger

Research output: Chapter in Book/Report/Conference proceedingChapter


In this tutorial, we will provide an introduction to the main Python software tools used for applying machine learning techniques to medical data. The focus will be on open-source software that is freely available and is cross platform. To aid the learning experience, a companion GitHub repository is available so that you can follow the examples contained in this paper interactively using Jupyter notebooks. The notebooks will be more exhaustive than what is contained in this chapter, and will focus on medical datasets and healthcare problems. Briefly, this tutorial will first introduce Python as a language, and then describe some of the lower level, general matrix and data structure packages that are popular in the machine learning and data science communities, such as NumPy and Pandas. From there, we will move to dedicated machine learning software, such as SciKit-Learn. Finally we will introduce the Keras deep learning and neural networks library. The emphasis of this paper is readability, with as little jargon used as possible. No previous experience with machine learning is assumed. We will use openly available medical datasets throughout.
Original languageEnglish
Title of host publicationMachine Learning for Health Informatics. Lecture Notes in Artificial Intelligence LNAI 9605
Place of PublicationCham
PublisherSpringer International
ISBN (Electronic)978-3-319-50478-0
ISBN (Print)978-3-319-50477-3
Publication statusPublished - 3 Feb 2017


  • Machine Learning
  • Data Science
  • Knowledge Extraction

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Experimental

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