Ingénieur IA associé pour les scientifiques de données

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Apprenez à regrouper, transformer, visualiser et extraire des informations à partir d'ensembles de données non marquées en utilisant scikit-learn et scipy.

Apprenez à créer votre premier réseau neuronal, à ajuster les hyperparamètres et à résoudre les problèmes de classification et de régression avec PyTorch.

Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.

Develop a multi-input model to classify characters from scanned documents.

Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

Use LLMs to solve diverse language tasks for a car dealership company.

Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating it within your stack.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Discover the fundamentals of Git for version control in your software and data projects.

Maîtrisez les tests Python : Apprenez des méthodes, créez des vérifications et assurez-vous d'un code sans erreur avec pytest et unittest.