Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data.
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| Title: | Hands-on 3: Introduction to developing Python computational pipelines for predictive machine learning modelling of livestock data. |
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| Authors: | Tulpan, Dan1 |
| Source: | Journal of Animal Science. 2024 Supplement, Vol. 102, p68-68. 1/4p. |
| Document Type: | Article |
| Subjects: | Machine learning, Personal computers, Animal science, Data modeling, Python programming language |
| Author-Supplied Keywords: | machine learning modelling Python |
| 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/ [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Animal Science is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Author Affiliations: | 1University of Guelph. |
| ISSN: | 0021-8812 |
| DOI: | 10.1093/jas/skae234.076 |
| Accession Number: | 179913431 |
| Database: | Veterinary Source |
| 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/ [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00218812 |
| DOI: | 10.1093/jas/skae234.076 |