etcML – Free Text Analysis Tool

etcML – Free Text-Analysis Tool

etcML Home Page

The etcML, Easy Text Classification with Machine Learning, website allows the user to upload their own data, and then, using the various built-in algorithms, run the already trained machine learning classifiers against that uploaded data to tag the text with sentiment (positive, negative or neutral), topic (such as politics, sports, business and the like) or with the user’s own classifiers. In addition to uploading data which is to be classified, the user is able to upload pre-labeled training data and train a classifier to predict tags for the uploaded raw data.

This is truly an amazing system. A tutorial on how to upload your own data and training data sets as well as create and train your own classifier is also available.

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Daniel Gutierrez, Managing Editor of insideBigData reports:

Have you every wondered whether a certain TV network has a specific political bias? Is your favorite news source fair and balanced? A group of Stanford computer scientists have created a website with the ability to answer such questions for free using machine learning technology.

The newly launched website is called etcML, short for Easy Text Classification with Machine Learning.

Machine learning is a field of computer science that gives computers the ability to acquire new understanding of data content in a more human-like way. The etcML website is based on machine-learning techniques that were developed to analyze the meaning embodied in text, then perform sentiment analysis – to gauge the text’s overall positive or negative sentiment.

Metacademy: Machine Learning and Probabilistic AI Learning Resources

I’ve recently come across a tremendous resource for the discovery of various machine learning and probabilistic artificial intelligence topics and associated educational materials. The site I’m describing is Metacademy.Metacademy Large Cropped Home PageMetacademy is a community-driven, open-source platform to facilitate the collaborative construction of a web of knowledge by domain experts meant to help individuals efficiently learn about any topic of interest (supported by Metacademy and the domain experts). The experts responsible for Metacademy are Roger Grosse and Colorado Reed. In addition to building the site, they organized roughly 350 machine learning and probabilistic artificial intelligence concepts along with related training and learning materials.

While Metacademy is currently focused on machine learning and probabilistic artificial intelligence topics, eventually, it has the goal to cover a much wider breadth of knowledge; e.g. mathematics, engineering, music, medicine, computer science, etc.

The premiss of Metacademy is that a user will search for and click on a concept of interest. Metacademy then produces a “learning plan” which includes the prerequisite concepts which were identified in the web of knowledge previously created by the domain experts. This component of identifying for the student the list of prerequisite concepts is what sets Metacademy apart from other learning sites or course catalogs.

As posted at Metacademy:
… But try learning something of conceptual depth by sifting through Google search results … and you’re in for a lot of agony. Before you learn this concept, you need to learn its prerequisite concepts (sometimes you’re not entirely sure what these are), and the prerequisite concepts may have prerequisites themselves. Pretty soon, you’re deep in dependency hell, switching between twenty different tabs trying to understand the various [pre]prerequisite concepts in order to understand the tutorial article Google returned …

Metacademy’s learning experience revolves around two central components:

  • a “learning plan” in a tabular ‘list view’
    Metacademy Logistic Regression List View
  • a “graph view” of the learning plan which is meant to help explore relationships among concepts
    Metacademy Logistic Regression Graph View

Clicking on the check-mark next to the title of a concept in either the graph or list view marks that [prerequisite] concept as being understood. To not show those concepts which have been marked as being understood, click the “hide” button in the upper right. Note that Metacademy will remember the concepts marked as understood and hidden and will automatically re-apply these selections at future visits.

As Metacademy is a work in progress and limited in scope, please keep an open mind when visiting, but I think that you will find it an interesting, unique and valuable resource, particularly if you are, as I am, actively exploring the world of machine learning.