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@ -12,14 +12,14 @@
## Index
* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
* [scikit-learn](#scikit-learn)
* [deep-learning](#deep-learning)
* [scikit-learn](#scikit-learn)
* [statistical-inference-scipy](#statistical-inference-scipy)
* [pandas](#pandas)
* [matplotlib](#matplotlib)
* [numpy](#numpy)
* [python-data](#python-data)
* [kaggle-and-business-analyses](#kaggle-and-business-analyses)
* [spark](#spark)
* [mapreduce-python](#mapreduce-python)
* [amazon web services](#aws)
@ -31,41 +31,6 @@
* [contact-info](#contact-info)
* [license](#license)
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
</p>
## kaggle-and-business-analyses
IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
| Notebook | Description |
|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
| [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. |
| [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.|
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
</p>
## scikit-learn
IPython Notebook(s) demonstrating scikit-learn functionality.
| 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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
<br/>
<p align="center">
<img src="http://i.imgur.com/ZhKXrKZ.png">
@ -158,6 +123,27 @@ Additional TensorFlow tutorials:
|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [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. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png">
</p>
## scikit-learn
IPython Notebook(s) demonstrating scikit-learn functionality.
| 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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png">
@ -272,6 +258,20 @@ IPython Notebook(s) demonstrating Python functionality geared towards data analy
| [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. |
| [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. |
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png">
</p>
## kaggle-and-business-analyses
IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses.
| Notebook | Description |
|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|
| [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. |
| [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.|
<br/>
<p align="center">
<img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/spark.png">