GitHub

MLJTutorial.jl

Tutorials for introducing the machine learning toolbox MLJ (Machine Learning in Julia)

Two collections of tutorials are hosted here:

For other MLJ learning resources see here. For experienced data scientists transitioning from another Machine Learning platform, we recommend MLJ for Data Scientists in Two Hours.

Also hosted here is a Lightning Tour of MLJ.

Using MLJ (video series)

Tutorials for use with the Using MLJ series of instructional videos.

MLJTutorial (original series)

Tutorials based on a 3.5 hour online workshop delivered in 2020, updated August 2026. These include a more detailed narrative and discuss some advanced features.

Basic

Advanced

Prerequisites

  • Familiarity with basic data manipulation in Julia: vectors, tuples, dictionaries, arrays, generating random numbers, tabular data (e.g., DataDrames.jl) basic linear algebra and statistics.

  • Familiarity with Julia package management

  • Familiarity with Machine Learning fundamentals and best practice.

Running tutorial code for yourself

You can find the annotated Julia scripts from which tutorials are generated in these directories. Package environment files for this repository are structured using Julia's package workspaces. To use with Julia 1.12 or later:

  1. Clone this GitHub repository to your computer

  2. In a terminal window change your directory to the root of the clone.

  3. From that terminal, launch julia (preferably with the --startup-file=no switch) and run using Pkg; Pkg.activate("."); Pkg.instantiate(workspace=true). This resolves a valid set of package for all the tutorials, downloads package code to your computer, and carries out some precompilation. This may take a few minutes. You need carry out this step only once.

  4. Activate the project for the particular tutorial of interest (as specified by Project.toml file in the corresponding directory).

  5. Execute code blocks copied from the rendered tutorial (as linked above) or from the corresponding .jl script.

More about the tutorials

  • The tutorials focus on the machine learning part of the data science workflow, and less on exploratory data analysis and other conventional "data analytics" methodology

  • Here "machine learning" is meant in a broad sense, and is not restricted to so-called deep learning (neural networks)

  • The tutorials are crafted to rapidly familiarize the user with what MLJ can do and how to do it, and are not a substitute for a course on machine learning fundamentals. Examples do not necessarily represent best practice or the best solution to a problem.

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This site makes use of an experimental, unregistered package NotebookManagementTools.jl.