Core functionality for the MLJ machine learning framework

A set of tutorials to show how to use Julia for data science (DataFrames, MLJ, ...)

An API for dispatching on the "scientific" type of data instead of the machine type

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

Home of the MLJ model registry and tools for model queries and mode code loading

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Parallel & lightning fast implementation of available classic and contemporary variants of the KMeans clustering algorithm

Julia Toolkit with fairness metrics and bias mitigation algorithms

Package providing K-nearest neighbor regressors and classifiers, for use with the MLJ machine learning framework.

SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language

MLJ Interface for ScikitLearn.jl

Connecting MLJ and MLFlow

MLJ.jl interface for GLM.jl models

An Introduction to Artificial Intelligence with Julia

MLJ.jl interface for JLBoost.jl

Repository implementing MLJ interface for MultivariateStats models.

One package to train them all

A Least Squares Support Vector Machine implementation in pure Julia

This open educational course introduces students and professionals to the fundamentals of data analysis and machine learning using the Julia programming language. Designed as a modern, hands-on guide, it walks participants through data preprocessing, visualization, and predictive modeling using Julia’s powerful ecosystem.

Julia learning resources collected from various Julia Computing repos!