|
12 | 12 |
|
13 | 13 | ## Index |
14 | 14 |
|
15 | | -* [kaggle-and-business-analyses](#kaggle-and-business-analyses) |
16 | | -* [scikit-learn](#scikit-learn) |
17 | 15 | * [deep-learning](#deep-learning) |
| 16 | +* [scikit-learn](#scikit-learn) |
18 | 17 | * [statistical-inference-scipy](#statistical-inference-scipy) |
19 | 18 | * [pandas](#pandas) |
20 | 19 | * [matplotlib](#matplotlib) |
21 | 20 | * [numpy](#numpy) |
22 | 21 | * [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/kaggle.png"> |
37 | | -</p> |
38 | | - |
39 | | -## kaggle-and-business-analyses |
40 | | - |
41 | | -IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses. |
42 | | - |
43 | | -| Notebook | Description | |
44 | | -|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| |
45 | | -| [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. | |
46 | | -| [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.| |
47 | | - |
48 | | -<br/> |
49 | | -<p align="center"> |
50 | | - <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png"> |
51 | | -</p> |
52 | | - |
53 | | -## scikit-learn |
54 | | - |
55 | | -IPython Notebook(s) demonstrating scikit-learn functionality. |
56 | | - |
57 | | -| Notebook | Description | |
58 | | -|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
59 | | -| [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. | |
60 | | -| [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. | |
61 | | -| [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. | |
62 | | -| [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. | |
63 | | -| [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. | |
64 | | -| [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. | |
65 | | -| [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. | |
66 | | -| [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. | |
67 | | -| [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. | |
68 | | - |
69 | 34 | <br/> |
70 | 35 | <p align="center"> |
71 | 36 | <img src="http://i.imgur.com/ZhKXrKZ.png"> |
@@ -158,6 +123,27 @@ Additional TensorFlow tutorials: |
158 | 123 | |--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
159 | 124 | | [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. | |
160 | 125 |
|
| 126 | +<br/> |
| 127 | +<p align="center"> |
| 128 | + <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/scikitlearn.png"> |
| 129 | +</p> |
| 130 | + |
| 131 | +## scikit-learn |
| 132 | + |
| 133 | +IPython Notebook(s) demonstrating scikit-learn functionality. |
| 134 | + |
| 135 | +| Notebook | Description | |
| 136 | +|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| 137 | +| [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. | |
| 138 | +| [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. | |
| 139 | +| [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. | |
| 140 | +| [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. | |
| 141 | +| [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. | |
| 142 | +| [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. | |
| 143 | +| [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. | |
| 144 | +| [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. | |
| 145 | +| [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. | |
| 146 | + |
161 | 147 | <br/> |
162 | 148 | <p align="center"> |
163 | 149 | <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 |
272 | 258 | | [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. | |
273 | 259 | | [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. | |
274 | 260 |
|
| 261 | +<br/> |
| 262 | +<p align="center"> |
| 263 | + <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/kaggle.png"> |
| 264 | +</p> |
| 265 | + |
| 266 | +## kaggle-and-business-analyses |
| 267 | + |
| 268 | +IPython Notebook(s) used in [kaggle](https://www.kaggle.com/) competitions and business analyses. |
| 269 | + |
| 270 | +| Notebook | Description | |
| 271 | +|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------| |
| 272 | +| [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. | |
| 273 | +| [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.| |
| 274 | + |
275 | 275 | <br/> |
276 | 276 | <p align="center"> |
277 | 277 | <img src="https://raw.githubusercontent.com/donnemartin/data-science-ipython-notebooks/master/images/spark.png"> |
|
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