Discover how machine learning is used to forecast network congestion, predict failures, and anticipate customer churn.
We will break down the three core types of ML: supervised, unsupervised, and reinforcement learning, with a focus on their practical telecom applications.
Learn how supervised learning uses labeled historical data to make accurate predictions about future network events.
Explore how unsupervised learning finds hidden patterns and anomalies in vast streams of network traffic data without prior labels.
Understand how reinforcement learning enables dynamic network optimization by making real-time decisions based on feedback from the environment.
This course provides the foundational knowledge to leverage AI for building smarter, self-optimizing telecommunications networks.