Machine Learning Fundamentals

Frame a Learnable Business Question

Build Features and Targets From Trustworthy Records

Separate Training, Validation, and Final Test Data

Establish Baselines and Measure Prediction Error

Fit a First Learned Model and Inspect Its Predictions

Recognize Underfitting and Overfitting

Evaluate Classification Decisions and Tradeoffs

Keep Learned Preprocessing Inside the Training Workflow

Use Cross-Validation to Check Stability

Choose a Model With a Small Documented Experiment

Check Limits, Subgroups, and Monitoring Needs

Capstone: Deliver a Reproducible First ML Experiment