# Julia implementations of some of the foundational Machine Learning models and algorithms from scratch

**URL:** <https://discourse.julialang.org/t/julia-implementations-of-some-of-the-foundational-machine-learning-models-and-algorithms-from-scratch/2586>\
**Category:** Machine Learning\
**Tags:** package\
**Created:** [March 10, 2017, 4:47am UTC](https://discourse.julialang.org/t/julia-implementations-of-some-of-the-foundational-machine-learning-models-and-algorithms-from-scratch/2586 "2017-03-10T04:47:54Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![memoiry](https://avatars.discourse-cdn.com/v4/letter/m/50afbb/32.png) [@memoiry](https://discourse.julialang.org/u/memoiry)\
**Post date:** [March 10, 2017, 4:47am UTC](https://discourse.julialang.org/t/julia-implementations-of-some-of-the-foundational-machine-learning-models-and-algorithms-from-scratch/2586/1 "2017-03-10T04:47:54Z")

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Hi

I have written a project [https://github.com/memoiry/lightML.jl](https://github.com/memoiry/lightML.jl) including basic implementation of typical machine learning algorithms implemented in Julia. It’s a self-educational project so that the implementation is quite basic, clear and easier to follow than the optimized libraries.

Also, I have wrapped the project into a Julia module, which makes it extremely easy to play with. For example, you can clone, load the module and then call test\_svm() for testing SVM. Every machine learning algorithm implemented now have a test function for testing purpose.

Hope someone could find it helpful and any contribution is welcome!