AI News, BOOK REVIEW: A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library

A Gentle Introduction to Scikit-Learn: A Python Machine Learning Library

If you are a Python programmer or you are looking for a robust library you can use to bring machine learning into a production system then a library that you will want to seriously consider is scikit-learn.

In 2010 INRIA got involved and the first public release (v0.1 beta) was published in late January 2010.

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

This means a deep focus on concerns such as easy of use, code quality, collaboration, documentation and performance.

Although the interface is Python, c-libraries are leverage for performance such as numpy for arrays and matrix operations, LAPACK, LibSVM and the careful use of cython.

Running this example produces the following output, showing you the details of the trained model, the skill of the model according to some common metrics and a confusion matrix.

If this is a small indication of companies that have presented on their use, then there are very likely tens to hundreds of larger organizations using the library.

recommend starting out with the quick-start tutorial and flicking through the user guide and example gallery for algorithms that interest you.

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