What’s New in Scikit-learn 0.15

Python has emerged as one of the more popular languages for doing data science. The primary reason is the impressive array of tools (the “Pydata” stack) available for addressing many stages of data science pipelines. One of the most popular Pydata tools is scikit-learn, an easy-to-use and highly-efficient machine learning library.

I’ve written about why I like to recommend scikit-learn so I won’t repeat myself here. Next week I’ll be hosting a FREE webcast featuring one of the most popular teachers and speakers in the Pydata community, scikit-learn committer Olivier Grisel:

This webcast will introduce scikit-learn, an Open Source project for Machine Learning in Python and review some new features from the recent 0.15 release such as faster randomized ensemble of decision trees and optimization for the memory usage when working on multiple cores.

We will also review on-going work part of the 2014 edition of the Google Summer of Code: neural networks, extreme learning machines, improvements for linear models, and approximate nearest neighbor search with locality-sensitive hashing.

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