DataScience VS ScikitLearn.jl

For all topics that explained briefly, I provided the links with more thorough documentation. In addition, I would highly recommend reading the Julia Data Science online book and learn the great set of machine learning examples in Julia Academy Data Science GitHub repository.

So, I just decided to try to learn Julia, and started by following the Julia for DataScience lectures on JuliaAcademy. In the first lecture, I get instructed to clone the DataScience repository on GitHub. According to instructions, I activated the environment with activate and check the status (status). I then ran instantiate to update any necessary packages, and get the following error message:

For machine learning, we will use SciKitLearn.jl library, which replicates SciKit-Learn library for Python. It provides an interface for commonly used machine learning models like Logistic Regression, Decission Tree or Random Forest. SciKitLearn.jl is not a single package but a rich ecosystem with many packages, and you need to select which of them to install and import. You can find a list of supported models here. Some of them are built-in Julia models, others are imported from Python. Also, the SciKitLearn.jl has a lot of tools to tune the learning process and evaluate results.

MLJ.jl is a good Julia ML framework. There's also a Scikitlearn.jl but its more of a wrapper around the sklearn I believe

That repo appears to be deprecated. Since you called using ScikitLearn, I imagine you should check this repo instead: https://github.com/cstjean/ScikitLearn.jl

Imho, Julia is more diverse in the sense that there is not a single popular ML library. Maybe the Julian equivalent for scikit-learn is MLJ.jl. There is also ScikitLearn.jl, which defines the usual interface of scikit-learn models, and specific algorithms then implement this interface.