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Added credits to a few sections. Added some placeholders for future scikit-learn topics.

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Donne Martin 2015-05-31 09:40:42 -04:00
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@ -90,12 +90,18 @@ 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 |
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [scikit-learn-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. |
| [scikit-learn-intro](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | K-Nearest Neighbors. |
| [scikit-learn-knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | K-Nearest Neighbors. |
| [scikit-learn-linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Linear regression. |
| [scikit-learn-svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Support vector machine classifier, with and without kernels. |
| [scikit-learn-random-forest](#scikit-learn) | Coming Soon. |
| [scikit-learn-k-means](#scikit-learn) | Coming Soon. |
| [scikit-learn-pca](#scikit-learn) | Coming Soon. |
| [scikit-learn-validation](#scikit-learn) | Coming Soon. |
<br/>
<p align="center">
@ -121,6 +127,8 @@ 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. |
@ -134,6 +142,8 @@ IPython Notebook(s) demonstrating matplotlib functionality.
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. |