This course introduces the fundamental concepts of Machine Learning and Deep Learning, providing a solid theoretical foundation and practical tools to understand and apply the main techniques of automatic learning. Participants will explore the phases of an ML project, from preprocessing operations to model evaluation, and learn how to implement supervised and unsupervised algorithms. The second part of the course focuses on Deep Learning, with an emphasis on neural networks and Convolutional Neural Networks (CNNs) for image classification. The program includes practical exercises in Python to consolidate the skills acquired.
Course Duration
8 hours
Target Audience
Individuals with a technical-scientific background
Requirements
Basic programming knowledge and elementary concepts of mathematics/statistics
Calendar
December 16th 2025 from 9:00 to 13:00
December 17th 2025 from 9:00 to 13:00