A Machine Learning Framework for Julia

MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about [200 machine learning models](https://alan-turing-institute.github.io/MLJ.jl/dev/model_browser/#Model-Browser) written in Julia and other languages. **New to MLJ?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/). **Integrating an existing machine learning model into the MLJ framework?** Start [here](https://alan-turing-institute.github.io/MLJ.jl/dev/quick_start_guide_to_adding_models/). **Wanting to contribute?** Start [here](CONTRIBUTING.md). **PhD and Postdoc opportunies** See [here](https://sebastian.vollmer.ms/jobs/). MLJ was initially created as a Tools, Practices and Systems project at the [Alan Turing Institute](https://www.turing.ac.uk/) in 2019. Current funding is provided by a [New Zealand Strategic Science Investment Fund](https://www.mbie.govt.nz/science-and-technology/science-and-innovation/funding-information-and-opportunities/investment-funds/strategic-science-investment-fund/ssif-funded-programmes/university-of-auckland/) awarded to the University of Auckland. MLJ has been developed with the support of the following organizations:

### The MLJ Universe The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. These repositories live at the [JuliaAI](https://github.com/JuliaAI) umbrella organization.

*Dependency chart for MLJ repositories. Repositories with dashed connections do not currently exist but are planned/proposed.*

Contributing • Code Organization • Road Map
#### Contributors *Core design*: A. Blaom, F. Kiraly, S. Vollmer *Lead contributor*: A. Blaom *Active maintainers*: A. Blaom, S. Okon, T. Lienart, D. Aluthge