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antropologiaNet, dataviz, collective intelligence, algorithms, social learning, social change, digital humanities
Curated by luiy
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A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)

A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python) | e-Xploration | Scoop.it
This tutorial is meant to help beginners learn tree based modeling from scratch. After the successful completion of this tutorial, one is expected to become proficient at using tree based algorithms and build predictive models.
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The SHOGUN #MachineLearning #Toolbox | #datascience

The SHOGUN #MachineLearning #Toolbox | #datascience | e-Xploration | Scoop.it

The Shogun Machine learning toolbox provides a wide range of unified and efficientMachine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.

 

One of Shogun's most exciting features is that you can use the toolbox through aunified interface from C++, Python, Octave, R, Java, Lua, C#, etc. This not just means that we are independent of trends in computing languages, but it also lets you use Shogun as a vehicle to expose your algorithm to multiple communities. We use SWIGto enable bidirectional communication between C++ and target languages. Shogun runs under Linux/Unix, MacOS, Windows.

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Overview of #Python Visualization Tools | #dataviz #datascience

Overview of #Python Visualization Tools | #dataviz #datascience | e-Xploration | Scoop.it
Overview of common python visualization tools
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Introduction

 

In the python world, there are multiple options for visualizing your data. Because of this variety, it can be really challenging to figure out which one to use when. This article contains a sample of some of the more popular ones and illustrates how to use them to create a simple bar chart. I will create examples of plotting data with:

 

- Pandas

- Seaborn

- ggplot

- Bokeh

- pygal

- Plotly

 

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Mining the Social Web. Blog, examples, code I #datascience #python

Mining the Social Web. Blog, examples, code I #datascience #python | e-Xploration | Scoop.it

Welcome to Mining the Social Web, a companion blog for the book with the simple purpose of taking social web mining mainstream. Valuable social data is scattered all across the web, and there is no...

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This page summarizes some instructions and helpful links for getting up and running with Mining the Social Web:

Bookmark and skim over the instructions at the Mining-the-Social-Web-2nd-Edition GitHub repositoryMining the Social Web is both a book and an open source software (OSS) project, and this is where you can download all of its source code. (Think of the book as a form of “premium support” for the OSS project.)Bookmark and watch the screencasts on the Mining the Social Web Vimeo channelThese short videos show you how to fully take advantage of the an incredible virtual machine experience that minimizes the friction involved in getting up and running with the source code. The Installing the Virtual Machine screencast is especially helpful as it provides a visual step-by-step process for installing the turn-key virtual machine.The IPython Notebook for Appendix A (Virtual Machine Experience) also provides written instructions for installing the virtual machine if you prefer that format.Download/preview Chapter 1 (Mining Twitter) of Mining the Social Web, 2nd Edition as a helpful introduction into social web miningDownload a DRM-free PDF from O’Reilly MediaView an HTML version in O’Reilly’s online ebook readerNew: Read along interactively with the full-text of Chapter 1 in IPython Notebook format that’s now checked in at GitHubRequires first installing the virtual machinePreview all of the source code for the entire book in a convenient IPython Notebook formatIndividual links that correspond to numbered examples from the book are  available at GitHub
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Intriguing Networks's curator insight, December 8, 2013 5:52 PM

Thanks Luiy for this post the use of apis to create Big Data, connect public engagement with the DH projects is an area I am particularly interested in and passing on.

 

The connect between the scholars and the networks that engage with and help crowdsource and socially develop the digital assets and platforms deployed has surely got to be of major value going forward.

 

For arts Science Humanities can see so much potential here and great to have access to this knowhow. 

 

The likes of Disqus et al are also still massively under-utilised I think but I bet if that hunch is wrong this community will be able to tell me why and where it is happening.

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scikit-learn: machine learning in #Python — #datascience

scikit-learn: machine learning in #Python — #datascience | e-Xploration | Scoop.it
scikit-learnMachine Learning in PythonSimple and efficient tools for data mining and data analysisAccessible to everybody, and reusable in various contextsBuilt on NumPy, SciPy, and matplotlibOpen source, commercially usable - BSD license
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Classification

Identifying to which set of categories a new observation belong to.

Applications: Spam detection, Image recognition.

Algorithms: SVM, nearest neighbors, random forest, ...

 

Regression

Predicting a continuous value for a new example.

Applications: Drug response, Stock prices.
Algorithms: SVR, ridge regression, Lasso, ...
...etc.
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#Python Packages For #DataMining

#Python Packages For #DataMining | e-Xploration | Scoop.it
Just because you have a “hammer”, doesn’t mean that every problem you come across will be a “nail”.

The intelligent key thing is when you use  the same hammer to solve what ever problem
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Topics: 

 

- why pyton?

 

- Librerias -- > NumPy, SciPy, Pandas, Matlotlib, Ipython, scikit-learn

 

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@WeAreTheDead : When #Twitter meets #Python! | #datascience

@WeAreTheDead : When #Twitter meets #Python! | #datascience | e-Xploration | Scoop.it

Reporters love Twitter and geeks love coding. Today, I’m merging the best of both worlds! On the menu: Python scripts to use Twitter to its full potential!

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When my friend @TerraCiolfe showed me @WeAreTheDeads project, I said to myself that I really need to learn how to control Twitter through Python. @WeAreTheDeads is a Twitter account publishing the name of a fallen soldiers at the 11th minute of each hour.

 

Of course, nobody is working behind the screen. A program chooses the soldier in a database and publishes his name, hour after hour. With 119,000 names to publish, the script will run until 2023, according to the author of this great idea, the reporter @GlenMcGregor from the Ottawa Citizen.

 

With a little bit of research (my sources are at the end of the article), I learnt how to work with Twitter from a Python script. Actually, we can do way more than automatically publish tweets! It’s also possible to extract a lot of data about users and their tweets. For example, you can research specific tweets in a specific location. I created a nice animated map at the end. You’ll see!

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#Mining the Social Web, 2nd-Edition | #datascience #SNA #tools

#Mining the Social Web, 2nd-Edition | #datascience #SNA #tools | e-Xploration | Scoop.it
Mining-the-Social-Web-2nd-Edition - The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)
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Chapter 0 - Preface

 

Chapter 1 - Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More

 

Chapter 2 - Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More

 

Chapter 3 - Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More

 

Chapter 4 - Mining Google+: Computing Document Similarity, Extracting Collocations, and More

 

Chapter 5 - Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts and More

 

Chapter 6 - Mining Mailboxes: Analyzing Who's Talking To Whom About What, How Often, and More

 

Chapter 7 - Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More

 

Chapter 8 - Mining the Semantically Marked-Up Web: Extracting Microformats, Inferencing Over RDF, and More

 

Chapter 9 - Twitter Cookbook

 

Appendix A - Virtual Machine Experience

Appendix B - OAuth Primer

Appendix C - Python & IPython Notebook Tips

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graph-tool: Efficent network analysis with python I #SNA #python

graph-tool: Efficent network analysis with python I #SNA #python | e-Xploration | Scoop.it
graph-tool: Efficent network analysis with python
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Graph-tool is an efficient Python module for manipulation and statistical analysis ofgraphs (a.k.a. networks). Contrary to most other python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance which is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

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