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Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5

Detalles Bibliográficos
Autor principal: Pastor Sanz, Iván
Lenguaje:eng
Publicado: Packt Publishing 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2677138
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author Pastor Sanz, Iván
author_facet Pastor Sanz, Iván
author_sort Pastor Sanz, Iván
collection CERN
id cern-2677138
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
publisher Packt Publishing
record_format invenio
spelling cern-26771382021-04-21T18:24:10Zhttp://cds.cern.ch/record/2677138engPastor Sanz, IvánMachine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5Computing and ComputersPackt Publishingoai:cds.cern.ch:26771382019
spellingShingle Computing and Computers
Pastor Sanz, Iván
Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title_full Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title_fullStr Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title_full_unstemmed Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title_short Machine learning with R quick start guide: a beginner's guide to implementing machine learning techniques from scratch using R 3.5
title_sort machine learning with r quick start guide: a beginner's guide to implementing machine learning techniques from scratch using r 3.5
topic Computing and Computers
url http://cds.cern.ch/record/2677138
work_keys_str_mv AT pastorsanzivan machinelearningwithrquickstartguideabeginnersguidetoimplementingmachinelearningtechniquesfromscratchusingr35