Milestone map
Milestone map
2 milestones
Do the course work in public
5–10 weeks (5–10 hrs/week)
Take Intro to Machine Learning for free and do the mini-projects (naive Bayes, SVM, trees, regression, clustering, feature selection, PCA, validation) and the final project. Keep everything in one public repo or notebook as you go.
Proof required
Link your public repo or notebook with the mini-projects (naive Bayes, SVM, trees, regression, clustering, feature selection, PCA, validation) and the final project, and a screenshot or log showing the self-check passed.
What gets checked
- Each mini-project's numbers match the course's quiz answers.
- The final project reports precision and recall on held-out data.
- Code is in a public repo with real history.
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 mini-projects (naive Bayes, SVM, trees, regression, clustering, feature selection, PCA, validation) and the final project.
Steps
- Make a public repo or notebook first.
- Do one unit at a time and commit after each.
- Run the self-check: Do each mini-project and check your answers in the course's quizzes.
- 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: Intro to Machine Learning (Udacity (free course)) 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
- Each mini-project's numbers match the course's quiz answers.
- The final project reports precision and recall on held-out data.
- Code is in a public repo with real history.
- Ask one question about a choice in the code; the answer should be clear.
You'll sign in first, then come straight back here.
Find a pattern in real data
1–3 weeks (5–8 hrs/week)
Use what you learned. Make a machine learning project on a public dataset that finds something useful (fraud, churn, spam), with the features you chose and why. Keep it small enough to finish in about two weeks.
Proof required
Link a machine learning project on a public dataset that finds something useful (fraud, churn, spam), with the features you chose and why, with a README that says how to run or read it.
What gets checked
- Features are explained, not just listed.
- Results are on held-out data.
- It says who could use the result and how.
Common mistakes
- A project too big to finish.
- A demo that only works on your machine.
Powstik Guide
A machine learning project on a public dataset that finds something useful (fraud, churn, spam), with the features you chose and why.
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
- Features are explained, not just listed.
- Results are on held-out data.
- It says who could use the result and how.
- Check that the work is the person's own.
You'll sign in first, then come straight back here.