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

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	https://github.com/marcosberghahn/PythonDataScienceHandbook#python-data-science-handbook
Python Data Science Handbook	http://shop.oreilly.com/product/0636920034919.do
	https://github.com/marcosberghahn/PythonDataScienceHandbook/blob/master/notebooks/figures/PDSH-cover.png
IPython	http://ipython.org
NumPy	http://numpy.org
Pandas	http://pandas.pydata.org
Matplotlib	http://matplotlib.org
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	https://github.com/marcosberghahn/PythonDataScienceHandbook#table-of-contents
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	https://github.com/marcosberghahn/PythonDataScienceHandbook#1-ipython-beyond-normal-python
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Computation on NumPy Arrays: Universal Functions	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.03-Computation-on-arrays-ufuncs.ipynb
Aggregations: Min, Max, and Everything In Between	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.04-Computation-on-arrays-aggregates.ipynb
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	https://github.com/marcosberghahn/PythonDataScienceHandbook#4-visualization-with-matplotlib
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Further Resources	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.15-Further-Resources.ipynb
5. Machine Learning	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.00-Machine-Learning.ipynb
	https://github.com/marcosberghahn/PythonDataScienceHandbook#5-machine-learning
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Introducing Scikit-Learn	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb
Hyperparameters and Model Validation	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb
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In-Depth: Naive Bayes Classification	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.05-Naive-Bayes.ipynb
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In-Depth: Decision Trees and Random Forests	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.08-Random-Forests.ipynb
In-Depth: Principal Component Analysis	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.09-Principal-Component-Analysis.ipynb
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In-Depth: k-Means Clustering	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.11-K-Means.ipynb
In-Depth: Gaussian Mixture Models	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.12-Gaussian-Mixtures.ipynb
In-Depth: Kernel Density Estimation	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.13-Kernel-Density-Estimation.ipynb
Application: A Face Detection Pipeline	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.14-Image-Features.ipynb
Further Machine Learning Resources	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.15-Learning-More.ipynb
Appendix: Figure Code	http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/06.00-Figure-Code.ipynb
	https://github.com/marcosberghahn/PythonDataScienceHandbook#appendix-figure-code
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requirements.txt	https://github.com/marcosberghahn/PythonDataScienceHandbook/blob/master/requirements.txt
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