Machine Learning: Classification

This module teaches you how to solve classification problems — predicting which discrete category a sample belongs to. You'll start from the first principles of what classification is, implement logistic regression as the primary binary classification algorithm, learn a comprehensive evaluation toolkit (confusion matrices, precision, recall, F1, AUC), and extend to multi-label and multi-class settings.

By the end of this module, you will be prepared to tackle the mushroom identification case study in module 10, where you'll apply these tools to a real-world binary classification problem.

Lessons

  1. Introduction to Classification — What classification is, the three types (binary, multi-class, multi-label), and the end-to-end scikit-learn pipeline.
  2. Binary Classification — Logistic regression, the confusion matrix, and the full suite of evaluation metrics.
  3. Multi-label Classification — Predicting multiple simultaneous labels per sample using MultiOutputClassifier and classifier chains.
  4. Multi-class Classification — Extending to three or more categories with One-vs-Rest, softmax, and multi-class evaluation.
  5. Module Assessment — Hands-on classification project using the mushroom dataset.