This course offers a practical introduction to statistical methods used in environmental sciences, using the R language. It is aimed at students and professionals who wish to master environmental data analysis.
This course teaches the basics of statistics applied to environmental sciences, using modern data processing tools in Python. Emphasis is placed on reproducibility and data visualisation.
This course introduces fundamental machine learning techniques through practical case studies in Python. It targets researchers, engineers, and students looking to harness the power of predictive algorithms.
This course explores deep neural networks and their applications in Python. It covers classical architectures (CNNs, RNNs) and widely-used tools such as TensorFlow and PyTorch.
This course offers a strategic and creative approach to generative AI, aiming to foster innovation, boost productivity, and support entrepreneurial initiatives.