- 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
- ML fundamentals from Dubai Expo by Alex Noyvirt and Claus Sthamer (UK, ONS) - available on youtube
- ML foundations for non-programmers by Tom Wise (UK ONS) - available on youtube
Course
- Machine Learning by Andrew Ng - available on youtube
- Statistical Learning by Trevor Hastie and Robert Tibshirani - available on youtube
- ML for Beginner by Microsoft - available on github
- ML Crash Course by Google - available online
- Machine learning by mathematicalmonk - available on youtube
- (application workshop) Satellite data analysis and machine learning classification with QGIS by AI for Good - available online
Blog
- Machine Learning Mastery - https://machinelearningmastery.com/start-here/
- Toward Data Science - https://towardsdatascience.com/
- Medium - https://medium.com/tag/machine-learning
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
Deep learning
- Deep learning: CS 182 Spring 2021 by UC Berkeley (Sergey Levine) - available on youtube
- Deep learning course Sprint 2020 by NYU (Yann LeCun) - available on youtube
- Deep learning for Coders with fastai and PyTorch by Fastai - available online
- Reinforcement lecture learning series by DeepMind - available online
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
- Python for Everyone (PY4E)
- 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
R tutorial
- Introduction to R by UK Data Science Campus - available on web (github link in the website)
Datasets
- ECOICOP data by Statistics Poland - available on Github
- Energy Balance Dataset by Belgium VITO - available on Zenodo
- List of datasets for machine learning research - wikipedia
Cross-cutting issues
Github
- Introduction to Github by Tabitha Williams and Brittny Vongdara (Statistics Canada) - available on youtub
MLOps
- ML-Ops.org - available online
- An Introduction to MLOps by AIEngineering - available on youtube
- Landscape of tools for ML, AI, Data, etc. - available online
Privacy
- Our Privacy Opportunity by OpenMinded - available online