Tag: Data Mining

"The web boom has taken the valley from its roots as a research haven to a consumer media app haven. Companies like Facebook and Twitter build simple apps, get traction, and then bring in the researchers. Nevertheless, I think we may be at the dawn of a data and research renaissance." by Bradford Cross.

"I have been thinking about learning and prediction as services for some time now. Like all good ideas, they tend to be thought of independently by several people when their time is ripe. Therefore I was not completely surprised when I heard the news yesterday that Google has released a new RESTful prediction API." by Mark Reid.

David Friedberg, the CEO of WeatherBill, and I recently talked about our experiences at WeatherBill and Flightcaster with applying machine learning to build predictive technology and wrap it in products.

"Python is the greatest thing to happen to computer science since the Turing Machine! Well, no, but it has inspired me into a personal renaissance for software writing. Its flexibility, widespread community support, and leveraging of legacy C and Fortran code also make it an outstanding language for social science researchers." via Zero Intelligence Agents.

MLcomp is a collaborative environment for objectively executing and comparing machine learning programs

Blog about cognition, systems, decisions, visualization, machine learning, etc.

So it’s pretty clear by now that statistics and machine learning aren’t very different fields.

Course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control.

Tutorial slides by Andrew Moore (professor of Robotics and Computer Science at Carnegie Mellon University) about decision trees, information gain, gaussians, and many many more topics related to the data mining.

Data presentation can be beautiful, elegant and descriptive. Smashing Magazine presented a variety of innovative and modern ways to visualize data.

Stephen Marsland shared all Python scripts from his book "Machine Learning: An Algorithmic Perspective".

Raymond Hettinger's presentation about Artificial Intelligence in Python. Presentation contains several basic AI techniques implemented with short, open-source Python code recipes.

Article illustrates an experiment - training laboratory standard rats in trading in the Foreign Exchange and Commodity Futures markets with the effect that managed to outperform some of the world's leading Human Fund managers.

Whoosh is a fast, featureful full-text indexing and searching library implemented in pure Python. Some of Whoosh's features include Pythonic API, pure-Python, fielded indexing and search, fast indexing and retrieval, pluggable scoring algorithm (text analysis, storage, posting format, etc.), and powerful query language.

PyMVPA (Python MultiVariate Pattern Analysis) is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms.