This course provides a comprehensive overview of the end-to-end machine learning lifecycle, from problem framing to model deployment and monitoring. You will begin by learning methods for the critical first step: collecting and preparing raw, messy telecom data for analysis. The curriculum then guides you through the entire pipeline, including feature engineering, model selection, training, and rigorous evaluation. All of these concepts are consolidated in a hands-on capstone project where you will build a functional predictive model for a real-world network analytics scenario. This project-centric approach ensures you gain practical experience in stitching together each component of the workflow into a cohesive solution. You will finish with the confidence to manage and execute your own ML projects from start to finish.