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

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Python Data Science Handbookhttp://shop.oreilly.com/product/0636920034919.do
https://github.com/EricSchles/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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https://github.com/EricSchles/PythonDataScienceHandbook#1-ipython-beyond-normal-python
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IPython and Shell Commandshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/01.05-IPython-And-Shell-Commands.ipynb
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2. Introduction to NumPyhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.00-Introduction-to-NumPy.ipynb
https://github.com/EricSchles/PythonDataScienceHandbook#2-introduction-to-numpy
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
Computation on Arrays: Broadcastinghttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.05-Computation-on-arrays-broadcasting.ipynb
Comparisons, Masks, and Boolean Logichttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/02.06-Boolean-Arrays-and-Masks.ipynb
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Further Resourceshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.13-Further-Resources.ipynb
4. Visualization with Matplotlibhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.00-Introduction-To-Matplotlib.ipynb
https://github.com/EricSchles/PythonDataScienceHandbook#4-visualization-with-matplotlib
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Text and Annotationhttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb
Customizing Tickshttp://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.10-Customizing-Ticks.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
https://github.com/EricSchles/PythonDataScienceHandbook#5-machine-learning
What Is Machine Learning?http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.01-What-Is-Machine-Learning.ipynb
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
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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
https://github.com/EricSchles/PythonDataScienceHandbook#appendix-figure-code
https://github.com/EricSchles/PythonDataScienceHandbook#required-packages
requirements.txthttps://github.com/EricSchles/PythonDataScienceHandbook/blob/master/requirements.txt
condahttp://conda.pydata.org
Managing Environmentshttp://conda.pydata.org/docs/using/envs.html
https://github.com/EricSchles/PythonDataScienceHandbook#license
https://github.com/EricSchles/PythonDataScienceHandbook#code
MIT licensehttps://github.com/EricSchles/PythonDataScienceHandbook/blob/master/LICENSE-CODE
Open Source Initiativehttps://opensource.org/licenses/MIT
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