In this presentation we overview the main Machine Learning (ML) workflows and their rapid specification through monadic packages. Additional facilitation is given by a Natural Language Processing (NLP) template engine and a Domain Specific Language (DSL) translator

We demonstrate that the ML workflows we consider are universal -- they have multi-language implementations in Python, R, WL, and others.

The overview covers Data wrangling, Classification, Regression, Latent Semantic Analysis, and Recommendations. Extensive explanations and examples are given in the book "Simplified Machine Learning Workflows."

This is the Raku-centric version of the talk I gave last week.

Please, buy me a coffee here: https://www.buymeacoffee.com/antonov70 .