How do you prevent overfitting when training machine learning models? Essentials

Start by picturing your model as a student who memorizes instead of learning: we’ll first show you how to spot the tell-tale gap between stellar training scores and poor test results. Then you’ll practice four practical fixes—simpler models, early stopping, regularization, and smart data splits—using hands-on demos you can run on your laptop. By the end you’ll confidently tune code and knobs to keep performance high on new, unseen data.