MACHINE LEARNING ENGINEER ROADMAP · STAGE 02 OF 04
Model training and evaluation
Learn supervised learning, baseline models, validation, and task-aligned evaluation.
Train a baseline, compare candidate models, and explain what the evaluation does and does not prove.
CORE SKILLS
Key concepts in model training and evaluation
Problem framing and baselines
Translate a product question into a prediction target, a simple baseline, and a measurable decision.
Topics include
- Classification and regression
- Target and label quality
- Baseline selection
Classical machine learning
Understand common model families and when their inductive assumptions make them useful.
Topics include
- Linear and tree-based models
- Feature preprocessing
- Regularization and model complexity
Validation and leakage
Design data splits that estimate generalization without allowing future or target information into training.
Topics include
- Train, validation, and test roles
- Cross-validation
- Time-aware splits and leakage checks
Metrics and thresholds
Choose evaluation measures and operating thresholds based on the consequences of different errors.
Topics include
- Precision, recall, and F1
- ROC/PR curves and calibration
- Segment-level error analysis
APPLY WHAT YOU LEARN
Practical project
Build a binary classifier with a documented baseline, leakage-safe split, metric chosen for the use case, and an error analysis of important cases.
CHECK YOUR UNDERSTANDING
You are ready to move on when
- Can justify the selected metric and threshold.
- Can describe validation limits and inspect more than the aggregate score.