- 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 HLG-MOS ML project team
- These resources are all freely available on open platform
Machine learning
Course
- Machine Learning by Andrew Ng - available on youtube
- Machine learning by mathematicalmonk - available on youtube
Blog
- Machine Learning Mastery - https://machinelearningmastery.com/start-here/
Book
- '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
References
- List of references provided in ML project reports and other sources - available on ML wiki
Python tutorial
- Coding and Classification kick-start tutorial for beginner by Statistics Poland - available on Google colab
- Fasttext tutorial by Statistics Canada - available on Github
- Autocoding class by ameasure - available on Github
- TensorFlow tutorial by Hvass Laboratories - available on Github
Datasets
Real data
- ECOICOP data by Statistics Poland - available on Github
- Energy Balance Dataset by Belgium VITO - available on Zenodo
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
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