Python - adeokar/adeokar.github.io GitHub Wiki
Packages and Tools
- LIBSVM - A Library for Support Vector Machines
- MLib - Spark's Scalable Machine Learning Library
- The Dato Machine Learning Platform
Getting Started / Help
The Hitchhiker’s Guide to Python!: This opinionated guide exists to provide both novice and expert Python developers a best-practice handbook to the installation, configuration, and usage of Python on a daily basis.
How to Develop Quality Python Code: Workflows and Development Tools, Main Site
Python Scientific Lecture Notes -> [Understanding indexing and slicing of arrays](http://scipy-lectures.github.io/intro/numpy/numpy.html#indexing-and- slicing)
[Python for Data Analysis: The Landscape of Tutorials](http://datacommunitydc.org/blog/2013/07/python-for- data-analysis-the-landscape-of-tutorials/)
[EnThought On Demand Training](https://training.enthought.com/?utm_source =academic-subscriber&utm_medium=email&utm_campaign=invite1#/courses)
[A gallery of interesting IPython Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting- IPython-Notebooks)
50 Examples for Teaching Python
Turtle Academy : The easy way to learn programming - Turtle Academy makes it surprisingly easy to start creating amazing shapes using the LOGO language
[Collecting Twitter data from the API using Python](http://nbvi ewer.ipython.org/github/alexhanna/hse-twitter/blob/master/docs/Collecting%20Tw itter%20data%20from%20the%20API%20with%20Python.ipynb)
Selected tutorials
- [Natural Language Processing and Big Data: Using NLTK and Hadoop – Talk Overview](http://datacommunitydc.org/blog/2013/05/nlp-and-big- data-using-nltk-and-hadoop-talk-overview/)
- Big Data and Natural Language Processing – Part 1
- The “Foo” of Big Data – Part 2
- Python’s Natural Language Took Kit (NLTK) and Hadoop – Part 3
- [Hadoop for Preprocessing Language – Part 4](http://datacommunitydc.org/blog/2013/05/hadoop-for-preprocessing- language/)
- Beyond Preprocessing – Weakly Inferred Meanings – Part 5
Using Python for scientific/engineering software development - from OpenOpt.org
Python Tutorial Videos http://pyvideo.org
Tutorial for NumPy [http://wiki.scipy.org/Tentative_NumPy_Tutorial](http://wiki.scipy.org/Tentati ve_NumPy_Tutorial)
Help for getting help on NumPy
In command prompt, you type ?
e.g., array?
Tutorial: Pandas
- 10 Minutes Pandas tutorial
- What is Pandas?
- Pandas Cookbook
- Pandas Tutorial
- Tutorial: pandas: Data Handling and Analysis in Python
- Tutorial with Example: [New York City Condominium Building Pricing Research (Downloaded using SourceTree)](https://github.com/seme0021/nyc- housing)
- [Readings CSV files with Pandas](http://pandas.pydata.org/pandas- docs/stable/generated/pandas.read_csv.html#pandas.read_csv)
- Multiindex-based Indexing (hierarchical indexing) in Pandas
- Benefits of panda's multiindex?
- Using SQL, Pandas, and Python to Work With Data
Tutorials: Scikit-Learn
- Kaggle: Getting Started with Python for Data Science
- http://blog.yhathq.com/posts/naive-bayes-in- python.html
- [http://blog.yhathq.com/posts/classification-using-knn-and- python.html](http://blog.yhathq.com/posts/classification-using-knn-and- python.html)
- [http://blog.yhathq.com/posts/predicting-customer-churn-with- sklearn.html](http://blog.yhathq.com/posts/predicting-customer-churn-with- sklearn.html)
- [http://nbviewer.ipython.org/github/yhat/DataGotham2013/blob/master/notebooks/ 4%20-%20scikit-learn%20basics.ipynb](http://nbviewer.ipython.org/github/yhat/D ataGotham2013/blob/master/notebooks/4%20-%20scikit-learn%20basics.ipynb)
- [http://blog.yhathq.com/posts/data-science-in-python- tutorial.html](http://blog.yhathq.com/posts/data-science-in-python- tutorial.html)
- [http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of- pipelines.html](http://zacstewart.com/2014/08/05/pipelines-of-featureunions- of-pipelines.html)
- [http://zacstewart.com/2013/11/27/kaggle-see-click-predict-fix- postmortem.html](http://zacstewart.com/2013/11/27/kaggle-see-click-predict- fix-postmortem.html)
- [http://www.ajbradley.com/blog/20130622](http://www.ajbradley.com/blog/2013062
- [http://nbviewer.ipython.org/github/fonnesbeck/Bios366/blob/master/notebooks/S ection6_1-Scikit-Learn.ipynb](http://nbviewer.ipython.org/github/fonnesbeck/Bi os366/blob/master/notebooks/Section6_1-Scikit-Learn.ipynb)
- [http://www.astro.washington.edu/users/vanderplas/speaking.html](http://www.as tro.washington.edu/users/vanderplas/speaking.html) - Number of tutorial videos on scikit-learn
- Exploring Machine Learning with Scikit-learn - PyCon 2014
- Jake VanderPlas - Machine Learning with Scikit-Learn (I) - PyCon 2015
- Data Science and (Unsupervised) Machine Learning with scikit-learn
- Tutorial: scikit-learn - Machine Learning in Python with Contributor Jake VanderPlas
- Introduction to Scikit Learn
- Scikit-learn to "learn them all"
- Python Machine learning with SKLearn Tutorial for Investing - Intro
- SciKit-Learn Laboratory
mlpy - Machine Learning Python
mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is Open Source, distributed under the GNU General Public License version 3.
Python Tools for Machine Learning
https://www.cbinsights.com/blog/python-tools-machine- learning/
RasberryPi
http://www.raspberrypi.org/help/what-is-a-raspberry- pi/
PYTHON FOR VISUAL BASIC PROGRAMMERS
http://www.raspberrypi.org/learning/python-for-vb- programmers/
Code Academy Python Course
[http://www.codecademy.com/en/tracks/python](http://www.codecademy.com/en/trac ks/python)
Bokeh - Python Toolkit for Visualization
StatsModels - Statistics in Python
[http://statsmodels.sourceforge.net/stable/index.html#](http://statsmodels.sou rceforge.net/stable/index.html#)
IPython Cookbook
The IPython Cookbook contains more than [100 recipes on numerical computing and data science with Python](http ://ipython-books.github.io/cookbook/#part-i-advanced-high-performance- interactive-computing). The integrality of the code is freely available on GitHub.
Markov Chain in Python
How Markov Chains Work
[https://www3.nd.edu/~tutorial/tutorial_files/markov/howItworks.html](https:// www3.nd.edu/~tutorial/tutorial_files/markov/howItworks.html)
Ben Langmead from Johns Hopkins University - https://github.com/BenLangmead
[http://nbviewer.ipython.org/gist/BenLangmead/7468937](http://nbviewer.ipython .org/gist/BenLangmead/7468937) - iPython Notebook for Markov Chain in Python
Markov Chain Explanation and Matlab guidelines
[http://www.haverford.edu/econ/econ365/Note%20on%20Markov%20Chains.pdf](http:/ /www.haverford.edu/econ/econ365/Note%20on%20Markov%20Chains.pdf)
Refer to Course Notes in "Haverford - Econ 365 - Comp Methods in Econ and Fin”
Downloaded from [http://www.haverford.edu/econ/econ365/index.php](http://www.h averford.edu/econ/econ365/index.php)
HMM with Weka
[http://stackoverflow.com/questions/11327707/what-is-the-equivalent-for-a -hidden-markov-model-in-the-weka- toolkit](http://stackoverflow.com/questions/11327707/what-is-the-equivalent- for-a-hidden-markov-model-in-the-weka-toolkit)
[http://doc.gold.ac.uk/~mas02mg/software/hmmweka/](http://doc.gold.ac.uk/~mas0 2mg/software/hmmweka/)
Wes McKinney - Pandas committer
Pythonic Perambulations - Musings and ramblings through the world of Python and beyond
Visualizing distributions of data
[http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distributions .html](http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distrib utions.html)
[http://stanford.edu/~mwaskom/software/seaborn/index.html](http://stanford.edu /~mwaskom/software/seaborn/index.html)
Bokeh - Visualization with Python
http://bokeh.pydata.org/en/latest/
Books
Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper
Kaggle
- Bike Sharing Demand: Forecast use of a city bikeshare system
[Continuations Made Simple and Illustrated Denys Duchier](https://www.ps.uni-saarland.de/~duchier/python/continuations.html)