Demystifies machine learning algorithms tools & gets you to apply them with Python

 Description

The aim of this course is to introduce the basic concepts of machine learning. We will have both a theoretical part with slides and a practical part where we will use the Python programming language for the exercises. In the theoretical part, we will introduce concepts such as supervised, unsupervised and reinforcement learning. A few examples of classic algorithms will be presented, such as tree-based models, naive Bayesian classification and neural networks. Without seeking to formalise too much, the theoretical aspects of machine learning will be presented both intuitively and using a mathematical formalism. For the programming part, the basic Python packages used in machine learning will be presented, but participants will be assumed to already have a good command of programming (ideally Python). By the end of the course, the aim is to have contributed to demystifying the tools used by data scientists and, in the advanced track, to have manipulated simple models on real data.

Course content

• Introduction to Machine Learning from Scratch
• Simple machine learning models
• The basics of deep learning
• Machine learning with Python

Learning outcomes

• Improve your knowledge and use of Python;
• Gain understanding of models and tools used by data scientists;
• Acquire a solid foundation in machine learning to pursue studies in the field ;
• Put your theoretical knowledge in programming into practice
• Gain confidence to apply and manipulate simple p
• Programming models on real data

Price

Thanks to the support of the European Commission and Innoviris in the framework of the EDIH sustAIn.brussels, SMEs and midcaps receive this training free of charge (0€), in the context of de minimis aid. Large companies and participants without a company pay 6746€ per participant.

Teachers

• Olivier CAELEN
• Yann-Aël LE BORGNE
• Jacopo DE STEFANI

Dates

• • Monday 10 March 2025 (9:00 - 16:00) - Onsite 
  • Wednesday 12 March 2025 (9:00 - 12:00) - Online
  • Friday 14 March 2025 (13:00 - 16:00)- Online 

*Please note that the schedule and location is subject to change and may be updated as necessary.