This course delves into the core mechanics of linear regression, teaching you how to construct robust predictive models for key telecom metrics like latency and bandwidth. You will learn to use hypothesis testing to statistically validate your model's predictions and ensure their reliability for decision-making. We demystify the cost function as the essential tool for quantifying prediction error and then employ gradient descent to automatically find the most accurate model parameters. A critical module is dedicated to diagnosing and solving the twin problems of overfitting and underfitting, ensuring your models generalize well to real-world network data. The curriculum blends statistical theory with practical coding exercises, all framed within common telecom analytics scenarios. You will finish with the skills to develop and critically assess data-driven models that can predict and enhance network performance.