Building a Portfolio for AI and Data Roles
Build two or three deep projects that show real judgement, not another tutorial clone
A taste of a lesson
My portfolio has five projects: Titanic, MNIST, Iris, house prices and a movie recommender. Is that enough?
Honestly, reviewers see those constantly, so they show you can follow tutorials but not much about your judgement. I'd swap them for two deeper projects. Pick a problem you care about, use data that's less common, beat a simple baseline, analyse the errors, and write up what you learned. Keep one classic project only if you took it somewhere new. What problems from your work, studies or hobbies could you get data for?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose project ideas that match your target role and stand out
- Build projects with baselines, proper evaluation and error analysis
- Write READMEs and write ups that reviewers can scan quickly
- Present each project as a short, convincing story in interviews
Lesson plan
- 1 What reviewers look for Understand how portfolios are reviewed and what signals judgement. Start
- 2 Choosing projects Pick two or three project ideas built on real problems and interesting data. Start
- 3 Baselines and evaluation Build credible results with baselines, validation and error analysis. Start
- 4 Code and reproducibility Make projects easy to run, read and trust. Start
- 5 README, demo and write up Communicate each project clearly and, where useful, let people try it. Start
- 6 Telling the project story Present projects confidently in applications and interviews. Start
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About this tutor
For students, graduates and career changers aiming at data science, ML engineering, analytics or LLM application roles. We design a small portfolio that interviewers actually look at: projects built around real problems, your own data collection or a less common dataset, clear evaluation, honest limitations, clean code and a readable README, and ideally something people can try. We critique your project ideas, avoid overused datasets, and plan how to present each project in applications and interviews. Honest feedback, no promises about outcomes.
Reviews
4.3
4 ratingsSample
- Nadia B.Sample
Replaced my tutorial projects with one on local air quality data. Interviewers asked about it in every conversation. Blunt feedback, but exactly what I needed.
- Owen P.Sample
Good advice but building deep projects takes far longer than I expected. Be ready to put in serious time.
- Jamal W.Sample
The README structure is excellent. Error analysis lesson made my project much stronger. Some sessions felt intense, in a good way.
- Svetlana M.Sample
Learned to start with a baseline. My fancy model barely beat it, which became the most interesting part of my write up.
About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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