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<a href="about_mlj"
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<a href="https://alan-turing-institute.github.io/MLJ.jl/dev/about_mlj/#Installation"
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<a href="https://alan-turing-institute.github.io/MLJ.jl/dev/about_mlj/#Learning-to-use-MLJ"
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<a href="mlj_cheatsheet" style="color: #9558B2;">Cheatsheet</a> |
<a href="common_mlj_workflows" style="color: #9558B2;">Workflows</a> |
<a href="https://github.com/alan-turing-institute/MLJ.jl/" style="color: #9558B2;">For Developers</a> |
<a href="https://mybinder.org/v2/gh/alan-turing-institute/MLJ.jl/master?filepath=binder%2FMLJ_demo.ipynb" style="color: #9558B2;">Live Demo</a> |
<a href="third_party_packages" style="color: #9558B2;">3rd Party Packages</a>
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MLJ</span>
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<span style="color: #9558B2;font-size:2.25em;font-style:italic;">
A Machine Learning Framework for Julia</span>
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Getting Started | Working with Categorical Data | Common MLJ Workflows | Machines | MLJ Cheatsheet
Working with Categorical Data | Preparing Data | Generating Synthetic Data | OpenML Integration
[Model Search](@ref model_search) | Loading Model Code | Transformers and Other Unsupervised Models | More on Probabilistic Predictors | Composing Models | Simple User Defined Models | [List of Supported Models](@ref model_list) | Third Party Packages
Evaluating Model Performance | Tuning Models | Controlling Iterative Models | Learning Curves
Composing Models | Linear Pipelines | Homogeneous Ensembles | Model Stacking |
Simple User Defined Models | Quick-Start Guide to Adding Models | Adding Models for General Use | Composing Models | [Internals](@ref internals_section) | Modifying Behavior
Weights | Acceleration and Parallelism | Performance Measures