Welcome to the SCAI Education platform!
Together, the Sorbonne University Alliance and the OpenClassrooms website offer you online modules to understand artificial intelligence, and learn its different uses and methods by yourself. Whether you are simply curious, an amateur or a professional in data sciences, you will be able to acquire skills while obtaining ECTS credits. You can switch from English to French using the "planet" icon at the top right of the screen. Evaluation is done through MCQs, peer-reviewed essays and notebooks (code sequences).
How to follow the teaching?
Choose a first module freely. Take all the course assessments for that module on OpenClassrooms, then upload your certificates under "OpenClassrooms Certificates" within the module on the SCAI platform to unlock the final assessment and your certificate.
Where to start?
If you have an OpenClassrooms license, all you have to do is follow the OpenClassrooms link, and choose your team to start the adventure.
If you do not have an OpenClassrooms license yet, please send your request to email@example.com or directly to your educational manager within the Sorbonne University partners network.
Skip available courses
This course consists in a series of 11 free online lessons from recognized AI specialists collaborating with the SCAI.
Before following this module, it is recommended to have followed the "machine learning (beginner)" module available here: https://scai-education.sorbonne-universite.fr/course/view.php?id=21
Are you ready to become machine learning experts? The "Machine Learning (Intermediate)" module is designed to help you deepen your knowledge of machine learning and teach you the skills you need to build successful machine learning models.
In the first course, you will learn how to clean and prepare your data for machine learning models. You will discover the best techniques for exploring and cleaning data, which will help you get more accurate and reliable results.
In the second course, you will discover how to evaluate the performance of your machine learning models. You will learn how to use different metrics to measure the quality of your predictions, as well as cross-validation techniques to estimate the performance of your models on unknown data.
With this module, you will have all the practical knowledge you need to develop better performing machine learning models. So, ready to take on the challenge and become machine learning experts? Let's go !
This course covers the use of Hugging Face libraries for building and deploying powerful AI systems for natural language processing, reinforcement learning, and interactive model interfaces. It includes the basics of natural langage processing, as well as advanced topics such as deep reinforcement learning and custom Gradio interfaces. The course is designed for students with a basic understanding of Python programming and machine learning concepts teached in the Machine Learning (introduction) module.