Reorder notebook sections
Donne Martin
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README.md
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README.md
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## Index
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* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
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* [scikit-learn](#scikit-learn)
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* [deep-learning](#deep-learning)
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* [scikit-learn](#scikit-learn)
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* [statistical-inference-scipy](#statistical-inference-scipy)
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* [pandas](#pandas)
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* [matplotlib](#matplotlib)
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* [numpy](#numpy)
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* [python-data](#python-data)
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* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
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* [spark](#spark)
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* [mapreduce-python](#mapreduce-python)
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* [amazon web services](#aws)
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@ -31,41 +31,6 @@
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* [contact-info](#contact-info)
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* [license](#license)
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
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</p>
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## kaggle-and-business-analyses
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IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
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| Notebook | Description |
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| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic. Learn data cleaning, exploratory data analysis, and machine learning. |
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| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn. Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
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</p>
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## scikit-learn
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IPython Notebook(s) demonstrating scikit-learn functionality.
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| Notebook | Description |
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|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [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. |
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| [knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | Implement k-nearest neighbors in scikit-learn. |
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| [linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Implement linear regression in scikit-learn. |
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| [svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Implement support vector machine classifiers with and without kernels in scikit-learn. |
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| [random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Implement random forest classifiers and regressors in scikit-learn. |
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| [k-means](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) | Implement k-means clustering in scikit-learn. |
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| [pca](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) | Implement principal component analysis in scikit-learn. |
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| [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |
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| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |
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<br/>
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<p align="center">
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<img src="http://i.imgur.com/ZhKXrKZ.png">
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@ -158,6 +123,27 @@ Additional TensorFlow tutorials:
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| [deep-dream](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/deep-dream/dream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
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</p>
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## scikit-learn
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IPython Notebook(s) demonstrating scikit-learn functionality.
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| Notebook | Description |
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|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| [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. |
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| [knn](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | Implement k-nearest neighbors in scikit-learn. |
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| [linear-reg](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) | Implement linear regression in scikit-learn. |
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| [svm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) | Implement support vector machine classifiers with and without kernels in scikit-learn. |
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| [random-forest](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) | Implement random forest classifiers and regressors in scikit-learn. |
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| [k-means](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) | Implement k-means clustering in scikit-learn. |
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| [pca](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) | Implement principal component analysis in scikit-learn. |
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| [gmm](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |
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| [validation](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png">
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@ -272,6 +258,20 @@ IPython Notebook(s) demonstrating Python functionality geared towards data analy
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| [pdb](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/pdb.ipynb) | Learn how to debug in Python with the interactive source code debugger. |
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| [unit tests](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/python-data/unit_tests.ipynb) | Learn how to test in Python with Nose unit tests. |
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
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</p>
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## kaggle-and-business-analyses
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IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
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| Notebook | Description |
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| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predict survival on the Titanic. Learn data cleaning, exploratory data analysis, and machine learning. |
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| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predict customer churn. Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.|
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<br/>
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<p align="center">
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<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/spark.png">
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