Machine Learning is about enabling systems to learn patterns from data and make decisions without being explicitly programmed for every scenario.
This section focuses on the complete machine learning pipeline — from understanding the problem and preparing data, to training models, evaluating performance, and interpreting results. The emphasis is on practical learning, where models are used to support real decisions rather than isolated experiments.
Projects here highlight how data transforms into insights through feature engineering, model selection, evaluation metrics, and iteration. The goal is not just to train models, but to understand their behavior, limitations, and real-world applicability.
