The Beta Machine Learning Toolkit is a package containing many algorithms and utilities for implementing machine learning workflows, such as supervised regression and classification, clustering, missing data imputation, dimensionality reduction, and various utilities for data transformation or prediction evaluation.
These algorithms are all self-contained in the library itself and coded in Julia, while the package can be easily used in Julia, Python, R and any other language with a Julia binding.
The goal of the library is to remain simple for casual users or standard ML analysis, while maintaining the flexibility required for more advanced analysis.
This talk gives an overview of the organisation of the library and some examples of use.
Alessandra Iacobucci et Pierre Navaro
