Complete the Machine Learning Foundations learning path
Finish by checking the evidence you produced, rerunning your capstone notebook, and writing a short responsible-use note for the model you studied.
No certificate or formal credentialSaved completion requires a learner login. Completion means your account has recorded the learning tasks you marked and the evidence you can explain.
Final checklist
- Your capstone notebook reruns cleanly from a restarted kernel.
- Your report names the problem, data limits, baseline, candidate model, metrics, errors, and responsible-use limits.
- Your conclusion avoids claiming causality, certainty, or real-world readiness from a small synthetic exercise.
- Your local ZIP copy is saved if you want to keep the notebooks.
Suggested next step
Move next into a deeper applied algorithms course only after this workflow feels clear: frame, audit data, split correctly, build baselines, use pipelines, compare honestly, inspect errors, and state limits.