This course introduces unsupervised learning, empowering you to extract insights from telecom data that has no predefined labels or categories. You will explore the concept of self-organizing networks (SON) and learn how clustering algorithms form the backbone of these automated systems. We provide a hands-on application of K-Means clustering to segment subscriber bases into distinct groups for targeted service offerings. The course covers the Gaussian distribution to help you model and understand the natural spread of network performance metrics like latency or signal strength. Finally, you will use this understanding to build powerful anomaly detection systems that automatically flag unusual network behavior and potential failures. You will learn to leverage these techniques to move from reactive problem-solving to proactive network management.