• Machine learning is widely used in many areas and there is not lack of resources if one wants to learn 
  • This wiki page contains few of introductory resources produced or recommended by ML community (HLG-MOS ML Project, ONS-UNECE ML Group)
  • These resources are all freely available on open platform

Machine learning 

From official statistics




  • 'The Elements of Statistical Learning: Data mining, Inference and Prediction', Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2009) - available online
  • 'An Introduction to Statistical Learning with Applications in R', Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013) - available online

Deep learning 

One user's experiences in learning ML 

  • Document describing how one user, who knew little about ML, got familiar and comfortable with ML using the product data and the code shared by the ML project. The document also presents a simulation on the integration of ML into a manual classification operation to achieve better accuracy at the same or lower cost. Many lessons learned are shared! - A user's experiences with the ML code and data shared
  • ECOICOP data by Statistics Poland - available on Github
  • ML Code from Statistics Poland - available on Github
  • ECOICOP alternate data - available on Github

Python tutorial

R tutorial

  • Introduction to R by UK Data Science Campus - available on web (github link in the website)


Cross-cutting issues  


  • Introduction to Github by Tabitha Williams and Brittny Vongdara (Statistics Canada) - available on youtub



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