Machine Learning Internship
The full machine learning pipeline, from messy raw data to a trained model and an evaluation that survives scrutiny.
What the Machine Learning track is.
A model with 99% accuracy on an imbalanced dataset can be completely useless, and plenty of projects ship exactly that. This track teaches you to spot it.
You take real, imperfect data through the whole pipeline: cleaning, features, training, and an evaluation that reports the metrics that actually matter for the problem rather than the one that looks best.
What you’ll work with
Three real projects, not three tutorials.
Every project below produces something that works and that you can explain. They are scoped to be finished, not to look impressive on a syllabus.
An end-to-end supervised model
Raw data through cleaning, features, training, and validation, with the choices at each step deliberate.
A feature engineering study
A measured comparison showing what your features actually contributed, rather than assuming they helped.
An honest evaluation report
Precision, recall, and the confusion matrix, including a clear statement of where the model fails.
What makes it worth your time.
You build, from day one
There is no classroom phase. You are given something real to build in your first week, and you build it.
Reviewed by a working engineer
Your work goes through review by someone who does this professionally, and you get told what is wrong with it.
Portfolio you can show
You finish with projects you can put in front of an employer and explain line by line, not a certificate alone.
Flexible around your studies
Short or long-term, remote, and scheduled around your academic calendar rather than against it.
Who this track is built for.
Students
Studying computer science or an adjacent field and want experience that looks like real work, not coursework.
Career changers
Moving into tech from somewhere else and need hands-on experience to make the move credible.
Self-taught developers
You have taught yourself the basics and want structure, review, and honest feedback on where you actually stand.
Fill out the application form and tell us what you’ve built.
Include anything you’ve built, broken, or measured yourself, a repo, a project, a course you finished. We care more about what you’ve actually attempted than where you’re studying. There are no fixed cohort dates; we take a small number of interns per track at a time.
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