Understand core ML concepts, data types, and the end-to-end ML workflow

Clean and prepare data using scaling, normalization, and standardization

Apply supervised learning: regression and classification algorithms

Apply unsupervised learning: clustering and anomaly detection

Understand reinforcement learning agents, rewards, and policies

Understand semi-supervised learning for scarce-label problems

Explore real aviation case studies across all four ML paradigms

Practice with interactive simulators for every module