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Title: GitHub - andersonsantos/PythonDataScienceHandbook: Jupyter Notebooks for the Python Data Science Handbook

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Python Data Science Handbookhttp://shop.oreilly.com/product/0636920034919.do
https://patch-diff.githubusercontent.com/andersonsantos/PythonDataScienceHandbook/blob/master/notebooks/figures/PDSH-cover.png
IPythonhttp://ipython.org
NumPyhttp://numpy.org
Pandashttp://pandas.pydata.org
Matplotlibhttp://matplotlib.org
Scikit-Learnhttp://scikit-learn.org
A Whirlwind Tour of Pythonhttps://github.com/jakevdp/WhirlwindTourOfPython
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Table of Contentshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb
https://patch-diff.githubusercontent.com/andersonsantos/PythonDataScienceHandbook#table-of-contents
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1. IPython: Beyond Normal Pythonhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb
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Help and Documentation in IPythonhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.01-Help-And-Documentation.ipynb
Keyboard Shortcuts in the IPython Shellhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.02-Shell-Keyboard-Shortcuts.ipynb
IPython Magic Commandshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.03-Magic-Commands.ipynb
Input and Output Historyhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.04-Input-Output-History.ipynb
IPython and Shell Commandshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb
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Understanding Data Types in Pythonhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.01-Understanding-Data-Types.ipynb
The Basics of NumPy Arrayshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb
Computation on NumPy Arrays: Universal Functionshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
Aggregations: Min, Max, and Everything In Betweenhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
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4. Visualization with Matplotlibhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb
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Customizing Matplotlib: Configurations and Stylesheetshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb
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Further Resourceshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb
5. Machine Learninghttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb
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Introducing Scikit-Learnhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb
Hyperparameters and Model Validationhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb
Feature Engineeringhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.04-Feature-Engineering.ipynb
In-Depth: Naive Bayes Classificationhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb
In-Depth: Linear Regressionhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.06-Linear-Regression.ipynb
In-Depth: Support Vector Machineshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.07-Support-Vector-Machines.ipynb
In-Depth: Decision Trees and Random Forestshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb
In-Depth: Principal Component Analysishttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb
In-Depth: Manifold Learninghttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.10-Manifold-Learning.ipynb
In-Depth: k-Means Clusteringhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb
In-Depth: Gaussian Mixture Modelshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb
In-Depth: Kernel Density Estimationhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb
Application: A Face Detection Pipelinehttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb
Further Machine Learning Resourceshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb
Appendix: Figure Codehttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb
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