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Applied the following discussed in the README to notebooks themselves vs the actual README links to the notebooks: Some notebooks I reference were created by other authors, who are credited within their notebook(s) by providing their names and/or a link to their source.

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Donne Martin 2015-06-14 06:26:34 -04:00
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@ -99,8 +99,6 @@ IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions.
IPython Notebook(s) demonstrating scikit-learn functionality.
Credits: Forked from [PyCon 2015 Scikit-learn Tutorial](https://github.com/jakevdp/sklearn_pycon2015) by Jake VanderPlas
| Notebook | Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) | Intro notebook to scikit-learn. Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |
@ -135,8 +133,6 @@ IPython Notebook(s) demonstrating pandas functionality.
IPython Notebook(s) demonstrating matplotlib functionality.
Credits: Some content forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel
| Notebook | Description |
|-----------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|
| [matplotlib](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb) | Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. |
@ -151,8 +147,6 @@ Credits: Some content forked from [Parallel Machine Learning with scikit-learn a
IPython Notebook(s) demonstrating NumPy functionality.
Credits: Forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel
| Notebook | Description |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [numpy](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |