The syllabus focuses mainly on the application of machine learning techniques using state-of-the-art libraries (TensorFlow, Keras, PyTorch, etc… as well as how to properly use and call large language models (LLM prompting and APIs).

Agenda for 20 hours:
- Introduction to Machine Learning
- Data Preprocessing.
- Supervised Learning
- Unsupervised Learning
- Introduction to Neural Networks
- Basic Reinforcement Learning
- Advanced ML Tools and Techniques
- Machine Learning in Production
- Working with Large Language Models (LLMs)