Machine Learning in Practice
From raw data to a working prediction — the full ML pipeline, hands-on.
What you'll actually learn
Most ML tutorials start with a clean CSV and a call to .fit(), which skips the part that actually eats the time: deciding what "raw data" even means, cleaning it, picking features that matter, and choosing a model that fits the problem instead of the one everyone's heard of. This track walks the full pipeline in order — ingestion, cleaning, feature engineering, training, evaluation — so each step has a reason instead of being a checkbox before the "real" work starts.
What you'll be able to do
You'll take a messy, real dataset with missing values and irrelevant columns, turn it into something a model can actually learn from, and explain — with real evaluation metrics, not "it looks about right" — why your model is or isn't good enough to trust. That's the difference between having run sklearn once and being someone a team can hand an actual prediction problem to.
Syllabus
Leads to
Frequently asked
Roughly 1 hour across 9 hands-on quests — you can go at your own pace and pick up exactly where you left off.
You should be comfortable with Python, AI: How It Actually Works, The Math of AI first — the skill tree unlocks Machine Learning in Practice once you've cleared those.
Yes — Machine Learning in Practice is fully available on the free tier, starting with a free first quest and no payment method required to sign up. Plus and Elite remove pacing limits but don't gate any of the Machine Learning in Practice curriculum behind a paywall.
Create a free account and begin your first quest — no card required.
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