MLJTutorial.jl

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

Based on tutorials originally part of a 3.5 hour online workshop.

Options for running the tutorials

Prerequisites

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

• Familiarity with Machine Learning fundamentals and best practice.

Topics covered

Basic

• Part 1 - Data Representation

• Part 2 - Selecting, Training and Evaluating Models

• Part 3 - Transformers and Pipelines

Advanced

• Part 4 - Tuning hyper-parameters

• Part 5 - Advanced model composition

The tutorials include links to external resources and exercises with solutions.

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.

Additional resources

• Slides from workshop given at ResBaz 2021 (Auckland)
• HelloJulia.jl - Resources from an Introduction to Julia workshop
• DataFrames.jl Cheatsheets
• MLJ Cheatsheet
• Common MLJ Workflows
• MLJ manual
• Data Science Tutorials in Julia

Credits

The author and maintainer of this repository is @ablaom. Pluto notebooks have been adapted from the julia scripts by @roland-KA who is also a maintainer.