This session explores the fundamentals of classification in machine learning and introduces practical techniques for evaluating model performance. Participants will learn how different classification algorithms approach decision-making, how decision boundaries are formed, and why no single model is best for all problems.

In addition, the session will cover essential evaluation methods used in real-world machine learning workflows, helping participants understand the strengths, limitations, and trade-offs of different models. The goal is to move beyond simply “training a model” and toward developing the judgment required to choose and evaluate models effectively.