From a24019419c2023a8c7cf0da17258310ba4d227b6 Mon Sep 17 00:00:00 2001 From: Donne Martin Date: Sun, 31 May 2015 09:40:42 -0400 Subject: [PATCH] Added credits to a few sections. Added some placeholders for future scikit-learn topics. --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 6e10f57..9e17adc 100644 --- a/README.md +++ b/README.md @@ -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. |

@@ -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. |