Python for Artificial Intelligence: From Data to Neural Networks Applied to the Agri-food Sector
Online/In-Person Course
Date and times
April 27, 28, 29 and 30, 2026
Schedule:
- Monday 27th from 4:00 PM to 8:30 PM (Online)
- Tuesday 28th from 9:00 to 13:00 h. (Online)
- Wednesday 24th from 11:00 to 14:00 and from 15:00 to 19:00 (In person)
- Thursday, the 30th, from 9:00 a.m. to 2:00 p.m. (In person)
Registration will be open until April 22, 2026.
Free training.
This course aims to provide a foundation for training in advanced digital skills specifically geared towards the agri-food sector, facilitating the acquisition of skills in data analytics, predictive modeling and the development of neural networks applied to real problems in the agri-food production environment.
More information on our website.

Who is it aimed at?
- Public administration technicians. Professionals involved in territorial or water planning and management who need to analyze data and support decision-making using Artificial Intelligence tools.
- Agrotech company personnel. Technical profiles that develop or implement technological solutions in the agricultural sector, interested in integrating data analysis and predictive models into their products.
- Cooperative technicians. Responsible for technical advice who seek to improve productive efficiency through the use of data and machine learning models.
- Irrigation community technicians. Water management specialists who require analysis and prediction tools to optimize irrigation planning and distribution.
- ICT company personnel. Technology professionals who wish to incorporate skills in data analysis and neural networks applied to the agro-industrial field.
- Agricultural engineers. Technicians in the agricultural sector interested in complementing their training with advanced data modeling and analysis tools.
- Farmers and ranchers with ICT skills . Digitally trained professionals in the primary sector who seek to apply Artificial Intelligence to improve the management and profitability of their farms.





