|
12 | 12 |
|
13 | 13 | ## Index |
14 | 14 |
|
15 | | -* [deep-learning](#deep-learning) |
16 | 15 | * [scikit-learn](#scikit-learn) |
| 16 | +* [kaggle-and-business-analyses](#kaggle-and-business-analyses) |
| 17 | +* [deep-learning](#deep-learning) |
17 | 18 | * [statistical-inference-scipy](#statistical-inference-scipy) |
18 | 19 | * [pandas](#pandas) |
19 | 20 | * [matplotlib](#matplotlib) |
20 | 21 | * [numpy](#numpy) |
21 | 22 | * [python-data](#python-data) |
22 | | -* [kaggle-and-business-analyses](#kaggle-and-business-analyses) |
23 | 23 | * [spark](#spark) |
24 | 24 | * [mapreduce-python](#mapreduce-python) |
25 | 25 | * [amazon web services](#aws) |
|
31 | 31 | * [contact-info](#contact-info) |
32 | 32 | * [license](#license) |
33 | 33 |
|
| 34 | +<br/> |
| 35 | +<p align="center"> |
| 36 | + <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png"> |
| 37 | +</p> |
| 38 | + |
| 39 | +## scikit-learn |
| 40 | + |
| 41 | +IPython Notebook(s) demonstrating scikit-learn functionality. |
| 42 | + |
| 43 | +| Notebook | Description | |
| 44 | +|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| 45 | +| [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. | |
| 46 | +| [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. | |
| 47 | +| [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. | |
| 48 | +| [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. | |
| 49 | +| [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. | |
| 50 | +| [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. | |
| 51 | +| [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. | |
| 52 | +| [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. | |
| 53 | +| [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. | |
| 54 | + |
| 55 | +<br/> |
| 56 | +<p align="center"> |
| 57 | + <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png"> |
| 58 | +</p> |
| 59 | + |
| 60 | +## kaggle-and-business-analyses |
| 61 | + |
| 62 | +IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses. |
| 63 | + |
| 64 | +| Notebook | Description | |
| 65 | +|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| |
| 66 | +| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning. | |
| 67 | +| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predicts customer churn. Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.| |
| 68 | + |
34 | 69 | <br/> |
35 | 70 | <p align="center"> |
36 | 71 | <img src="http://i.imgur.com/ZhKXrKZ.png"> |
@@ -93,27 +128,6 @@ IPython Notebook(s) demonstrating deep learning functionality. |
93 | 128 | |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
94 | 129 | | [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. | |
95 | 130 |
|
96 | | -<br/> |
97 | | -<p align="center"> |
98 | | - <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png"> |
99 | | -</p> |
100 | | - |
101 | | -## scikit-learn |
102 | | - |
103 | | -IPython Notebook(s) demonstrating scikit-learn functionality. |
104 | | - |
105 | | -| Notebook | Description | |
106 | | -|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
107 | | -| [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. | |
108 | | -| [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. | |
109 | | -| [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. | |
110 | | -| [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. | |
111 | | -| [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. | |
112 | | -| [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. | |
113 | | -| [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. | |
114 | | -| [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. | |
115 | | -| [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. | |
116 | | - |
117 | 131 | <br/> |
118 | 132 | <p align="center"> |
119 | 133 | <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scipy.png"> |
@@ -189,20 +203,6 @@ IPython Notebook(s) demonstrating Python functionality geared towards data analy |
189 | 203 | | [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. | |
190 | 204 | | [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. | |
191 | 205 |
|
192 | | -<br/> |
193 | | -<p align="center"> |
194 | | - <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png"> |
195 | | -</p> |
196 | | - |
197 | | -## kaggle-and-business-analyses |
198 | | - |
199 | | -IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses. |
200 | | - |
201 | | -| Notebook | Description | |
202 | | -|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| |
203 | | -| [titanic](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/kaggle/titanic.ipynb) | Predicts survival on the Titanic. Demonstrates data cleaning, exploratory data analysis, and machine learning. | |
204 | | -| [churn-analysis](http://nbviewer.ipython.org/github/donnemartin/data-science-ipython-notebooks/blob/master/analyses/churn.ipynb) | Predicts customer churn. Exercises logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors. Discussion of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration/descrimination.| |
205 | | - |
206 | 206 | <br/> |
207 | 207 | <p align="center"> |
208 | 208 | <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/spark.png"> |
|
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