Reference : Scikit-Learn: Machine Learning in the Python ecosystem
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/162794
Scikit-Learn: Machine Learning in the Python ecosystem
English
Joly, Arnaud mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
27-Jan-2014
No
National
GIGA DAY 2014
27-01-2014
Liège
Belgium
[en] machine learning ; open source software
[en] The scikit-learn project is an increasingly popular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of contexts. The primary aim of the project is to provide a compendium of efficient implementations of classic, well-established machine learning algorithms. Among other things, it includes classical supervised and unsupervised learning algorithms, tools for model evaluation and selection, as well as tools for data preprocessing and feature engineering.

This presentation will illustrate the use of scikit-learn as a component of the larger scientific Python environment to solve complex data analysis tasks. Examples will include end-to-end workflows based on powerful and popular algorithms in the library. Among others, we will show how to use out-of-core learning with on-the-fly feature extraction to tackle very large natural language processing tasks, how to exploit an IPython cluster for distributed cross-validation, or how to build and use random forests to explore biological data.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/2268/162794
See also http://orbi.ulg.ac.be/handle/2268/157487

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