ScikitLearn.jl VS NumPy

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.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]