Machine Learning for Health Informatics pp 435-480

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9605) | Cite as

A Tutorial on Machine Learning and Data Science Tools with Python

Chapter

Abstract

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.

Keywords

Machine learning Deep learning Neural networks Tools Languages Python 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Holzinger Group HCI-KDD, Institute for Medical Informatics, Statistics and DocumentationMedical University of GrazGrazAustria

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