https://github.com/roland-ka/juliaformltutorial

A tutorial that shows how Julia and its ecosystem can be used for Machine Learning
https://github.com/roland-ka/juliaformltutorial

julia machine-learning tutorial

Last synced: 1 day ago
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A tutorial that shows how Julia and its ecosystem can be used for Machine Learning

• Host: GitHub
• URL: https://github.com/roland-ka/juliaformltutorial
• Owner: roland-KA
• License: mit
• Created: 2022-03-16T18:09:01.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-03-17T21:04:26.000Z (over 2 years ago)
• Last Synced: 2023-08-07T16:59:56.660Z (over 1 year ago)
• Topics: julia, machine-learning, tutorial
• Language: Julia
• Homepage:
• Size: 48.8 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# JuliaForMLTutorial

I have published in *Towards Data Science* the following tutorial that shows how Julia and its ecosystem can be used for Machine Learning:

- [Part I - *Analyzing the Glass dataset*](https://towardsdatascience.com/part-i-analyzing-the-glass-dataset-c556788a496f) concentrates on how data can be preprocessed, analyzed and visualized using packages like `ScientificTypes`, `DataFrames`, `StatsBase` and `StatsPlots`.

- [Part II - *Using a Decision Tree*](https://towardsdatascience.com/part-ii-using-a-decision-tree-ddffa4004e47) focuses on the core of the ML workflow: How to choose a model and how to use it for training, predicting and evaluating. This part relies mainly on the package `MLJ` (= Machine Learning in Julia).

- [Part III - *If things are not 'ready to use'*](https://towardsdatascience.com/part-iii-if-things-are-not-ready-to-use-59d2db378bec) explains how easy it is to create your own solution with a few lines of code, if the packages available don't offer all the functionality you need.

In this repository you find for each of the three parts a Pluto notebook (in `notebooks`) with all code examples used in the tutorial.