Machine Learning in Telecommunication: From Basics to Real-World Cases

This intermediate-level course provides a focused exploration of how machine learning (ML) transforms modern telecommunications networks. Designed for students and professionals with foundational knowledge of telecom or AI who want to deepen their understanding of ML applications in network optimization, predictive analytics, and intelligent automation, the course covers several key machine learning paradigms: supervised, unsupervised, and reinforcement learning. Through real-world case studies, explore key ML concepts like regression, classification, clustering, hypothesis testing, cost functions, gradient descent, and model evaluation. Learn how models predict telecom metrics such as signal strength, network load, and bandwidth demand, and how classification techniques help detect faults and anomalies. By the end of this course, you’ll be equipped with the skills you need to leverage ML to power smarter and more adaptive telecom networks.
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