Accompanying Jupyter notebooks for the Springer LNAI 9605 book chapter.
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LinearRegression.ipynb
NumPy.ipynb
Pandas.ipynb
Plotting.ipynb
README.md

README.md

Book Chapter Companion Repository

This repository contains the accompanying Jupyter notebooks for the book chapter titled A Tutorial on Machine Learning and Data Science Tools with Python, pp. 435–480, Machine Learning for Health Informatics, Volume 9605, Lecture Notes in Computer Science, Springer, 2016. ISBN: 978-3-319-50477-3.

See http://link.springer.com/chapter/10.1007/978-3-319-50478-0_22.

Notebooks

Notebooks are currently being updated and will be uploaded in the coming days

The following table indicates which notebook accompanies which section of the book chapter:

Section Pages Section Title Notebook
7.2 448–450 NumPy NumPy.ipynb
7.3 450–456 Pandas Pandas.ipynb
8 456–457 Data Visualisation and Plotting Plotting.ipynb
9.2 458–462 Linear Regression LinearRegression.ipynb
9.3 462–467 Non-Linear Regression and Model Complexity Not yet live.
9.4 467–468 Clustering Not yet live.
9.5 468–469 Classification Not yet live.
9.6 470–472 Dimensionality Reduction Not yet live.
10 472–476 Neural Networks and Deep Learning Not yet live.

Citing the Book Chapter

To cite the paper, you can use:

@Inbook{Bloice2016,
author="Bloice, Marcus D.
and Holzinger, Andreas",
editor="Holzinger, Andreas",
title="A Tutorial on Machine Learning and Data Science Tools with Python",
bookTitle="Machine Learning for Health Informatics: State-of-the-Art and Future Challenges",
year="2016",
publisher="Springer International Publishing",
pages="435--480",
isbn="978-3-319-50478-0",
doi="10.1007/978-3-319-50478-0_22",
url="http://dx.doi.org/10.1007/978-3-319-50478-0_22"
}