Learning Tracks
Your roadmap to real skills.
Many tracks and dozens of sub-tracks. Every track starts with a free intro lesson.
Learning Tracks
Many tracks and dozens of sub-tracks. Every track starts with a free intro lesson.
The rigor reviewers demand: metrics, contamination, significance, reproducibility.
~11 hours · 8 lessons
View track details →Why Evaluation Is the Hard Part
Understand why evaluation, not ideas, is where most rejected papers fail.
Automatic Metrics
Use and critique the standard automatic metrics and their failure modes.
Benchmarks and Contamination
Use NLP benchmarks responsibly and detect/avoid data contamination.
Human Evaluation and LLM-as-Judge
Design a human evaluation with inter-annotator agreement and use LLM-as-judge despite its biases.
Statistical Rigor
Apply significance testing, confidence intervals, multiple seeds, and ablations.
Reproducibility
Make an experiment reproducible: seeds, pinned envs, model/data cards, code release.
Passion Project: Evaluation Harness
Build a reusable harness that computes metrics with confidence intervals and significance tests.
Evaluation Job Readiness
Translate evaluation rigor into the research-maturity signal advisors and panels screen on.