5 Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data
Abstract This hands-on workshop aims to offer examples of computational pipelines that apply traditional predictive machine learning modelling techniques to animal science datasets with the purpose of solving classification and/or regression problems. The objective of this workshop is to provide the attendants with fully functional and customizable computational pipelines developed in Python. The workshop is structured in a hands-on format and includes a combination of basic notions about machine learning and relevant algorithms, evaluation measures, evaluation strategies and Python code examples. To avoid technical problems related to installing a Python environment on personal computers, we recommend the registrants to acquire access on the Repl.it platform (https://replit.com/) by creating a free account prior to attending the workshop. Detailed information will be provided before the beginning of the workshop at the following URL: http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2024/
Authors
- Dan Tulpan (ORCID: https://orcid.org/0000-0003-1100-646X)
Institutions
- University of Guelph (CA)
Publication Details
- Journal
- Journal of Animal Science
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1093/jas/skae234.076
- Primary Topic
- Genetic and phenotypic traits in livestock
- Type
- article
- Field-Weighted Citation Impact
- 0.00