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Building a Machine Learning Portfolio Print

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Demonstrating capability.

WHAT DEMONSTRATES LITTLE

Tutorial reproductions Well-known teaching datasets Notebooks with no context

WHY

Everyone has those, and they show no judgement.

WHAT DEMONSTRATES CAPABILITY

A problem you framed yourself Messy data you obtained and cleaned Honest evaluation, including what failed A deployed, working system Documentation explaining the decisions

WHAT TO DEPLOY

Something small, reachable at a link.

WHY

It proves you can get past the point most projects die.

WHAT TO WRITE ALONGSIDE

Why you framed the problem as you did What you tried that did not work What the limitations are

WHY FAILURES SPECIFICALLY

They demonstrate judgement, which successes alone do not.

WHAT PROBLEMS TO CHOOSE

Ones from a domain you understand Ones using data you can legitimately obtain Ones small enough to complete

WHAT TO AVOID

Projects requiring data you cannot get Projects too large to finish

WHAT TO INCLUDE FOR LOCAL RELEVANCE

Problems from the local context, which are distinctive and less commonly attempted.


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