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AI and data syllabi
Each course outline is free to view. The full PDF syllabus is available after payment, as an instant download.
Generative AI and Deep Learning
Generative AI: from LLMs to agents
Understand and use generative models: LLMs, prompt engineering, RAG, agents and deployment, with hands-on Python labs.
View the outlineDeep Learning with TensorFlow and Keras
Design, train and evaluate deep neural networks with TensorFlow 2 and Keras, from dense networks to convolutional and recurrent architectures.
View the outlineAdvanced Deep Learning with PyTorch
Go further in deep learning with PyTorch: custom training loops, transfer learning, modern architectures and performance tuning.
View the outlineData, Big Data and MLOps
Big Data and Data Science
Big Data and data science fundamentals: the ecosystem, distributed storage, data preparation and first large-scale processing.
View the outlinePython, Spark and advanced AI projects
Process massive datasets with PySpark and apply machine learning to them, up to full AI projects on large-scale data.
View the outlineEnd-to-end MLOps
Take a model from notebook to production: experiment tracking, containers, orchestration and deployment, with a final ticket-classification project.
View the outlineAI innovation project in 5 days
An intensive capstone module: five ready-to-run topics, from data to deployment, with containerized stacks, deliverables and grading rubrics.
View the outlineMathematics, tech watch and ethics
Mathematics for ML and Deep Learning
The mathematical foundations of deep learning: linear algebra and SVD, multivariate calculus, optimization and information theory, with Python tutorials.
View the outlineTech watch and data ethics
Run an active tech watch on data and AI, evaluate new tools for the company, and build GDPR, bias and carbon impact into projects.
View the outline