ML Zoomcamp announcements

We start Module 4 on Evaluation Metrics for Classification. Once you have a classification model, you need to know how well it performs. Accuracy alone can be misleading, especially when classes are imbalanced. With the right metrics, you can compare models and report performance you can trust. We cover: - Accuracy, precision, recall, and F1 score - Confusion matrices - ROC curves and AUC - Cross-validation - Comparing against a dummy baseline Materials: https://github.com/DataTalksClub/machine-learning-zoomcamp/tree/main/04-evaluation Homework 4 evaluates models on the 2026 lead-scoring dataset. Homework: https://github.com/DataTalksClub/machine-learning-zoomcamp/blob/main/cohorts/2026/homework/04-evaluation/homework.md Submit: https://courses.datatalks.club/ml-zoomcamp-2026/homework/hw04 Deadline: October 20, 2026 at 01:00 CEST (October 19 at 23:00 UTC). Next week, Module 5 covers deploying machine learning models. Questions go in the course Slack channel: https://app.slack.com/client/T01ATQK62F8/C0288NJ5XSA