MLJTutorial.jl

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

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# MLJTutorial.jl

Notebooks for introducing the machine learning toolbox
[MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) (Machine
Learning in Julia)

Based on tutorials originally part of a 3.5 hour [online
workshop](https://github.com/ablaom/MachineLearningInJulia2020).

#### [Options for running the tutorials](options.md)

### 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](slides/slides.pdf) from workshop given at ResBaz 2021 (Auckland)
- [HelloJulia.jl](https://github.com/ablaom/HelloJulia.jl) - Resources from an Introduction to Julia workshop 
- [DataFrames.jl Cheatsheets](https://ahsmart.com/pub/data-wrangling-with-data-frames-jl-cheat-sheet/index.html)
- [MLJ Cheatsheet](https://alan-turing-institute.github.io/MLJ.jl/dev/mlj_cheatsheet/)
- [Common MLJ Workflows](https://alan-turing-institute.github.io/MLJ.jl/dev/common_mlj_workflows/)
- [MLJ manual](https://alan-turing-institute.github.io/MLJ.jl/dev/)
- [Data Science Tutorials in Julia](https://juliaai.github.io/DataScienceTutorials.jl/)

### 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.

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