Milestone map
Milestone map
2 milestones
Do the course work in public
6–11 weeks (3–5 hrs/week)
Take Statistical Learning with Python for free and do the labs and applied exercises for the chapters on regression, classification, resampling, trees and unsupervised learning. Keep everything in one public repo or notebook as you go. OSSU links the edX page; we link the authors' free site, which has the same videos, book and labs.
Proof required
Link your public repo or notebook with the labs and applied exercises for the chapters on regression, classification, resampling, trees and unsupervised learning, and a screenshot or log showing the self-check passed.
What gets checked
- Lab results match the book's.
- Applied exercises answer every part, in plain words.
- Train and test data are kept apart.
Common mistakes
- Copying posted answers: verifiers read the history.
- Skipping the self-check, then asking for review too early.
Powstik Guide
A public repo or notebook with the labs and applied exercises for the chapters on regression, classification, resampling, trees and unsupervised learning.
Steps
- Make a public repo or notebook first.
- Do one unit at a time and commit after each.
- Run the self-check: Run each chapter's lab and match its printed results, then do the chapter's applied exercises.
- Write a short README: what is here and how to run it.
- Ask three people who know the topic to check it.
Template
README Course: Statistical Learning with Python (An Introduction to Statistical Learning (free book, videos and labs)) What is here: How to run it: Self-check result: What was hardest:
What gets sent back
- No history, just one big upload.
- Self-check not shown.
- Code that does not run from the README.
Resources
Foundationstart here
Depthgo deeper
What a verifier looks for
- Lab results match the book's.
- Applied exercises answer every part, in plain words.
- Train and test data are kept apart.
- Ask one question about a choice in the code; the answer should be clear.
You'll sign in first, then come straight back here.
Compare models on a real question
1–3 weeks (5–8 hrs/week)
Use what you learned. Make a notebook that tries three methods from the book on a real dataset, compares them fairly with cross-validation and picks one. Keep it small enough to finish in about two weeks.
Proof required
Link a notebook that tries three methods from the book on a real dataset, compares them fairly with cross-validation and picks one, with a README that says how to run or read it.
What gets checked
- The comparison uses the same folds for every method.
- The pick is explained, including what it gets wrong.
- Others can rerun it.
Common mistakes
- A project too big to finish.
- A demo that only works on your machine.
Powstik Guide
A notebook that tries three methods from the book on a real dataset, compares them fairly with cross-validation and picks one.
Steps
- Write one line: what it does and for whom.
- Build the smallest version that works.
- Test it on someone else's machine or with someone else reading it.
- Write the README.
- Ask three people to check it.
Template
README What it does: Who it is for: How to run it: What I would do next:
What gets sent back
- Does not run from the README.
- Not clearly the person's own work.
- Claims more than it does.
Resources
Foundationstart here
Depthgo deeper
What a verifier looks for
- The comparison uses the same folds for every method.
- The pick is explained, including what it gets wrong.
- Others can rerun it.
- Check that the work is the person's own.
You'll sign in first, then come straight back here.