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YOUR LEARNING PATH

Applied Machine Learning

Move from a clear prediction problem to an evaluated model. Practice baselines, leakage-safe pipelines and responsible error analysis.

6 steps / 4-6 months estimated
Before you start
Comfort with Python, pandas and basic statistics: averages, distributions and probability. Start with the data analysis roadmap.
What you will build
An evaluated classification project with a baseline, a leakage-safe pipeline, error analysis and a model card explaining limitations.
STEP 01 OF 6

Define a useful prediction problem

Choose a low-stakes learning task, such as classifying sample flower measurements. Identify what a prediction means and when the input becomes available.

Break it down

01Build it yourself

Write a one-page problem statement: who would use the result, what the input and target represent, and what a wrong prediction would cost. Use a documented teaching dataset, not private student records or a real eligibility decision.

02Ready to move on?

Explain why prediction is useful here and when a simple rule would be enough. Record dataset origin, permitted use and limitations before training.

Take it further

Continue learning on these source websites.

scikit-learn: getting started and evaluation(opens in a new tab)
PUT IT INTO PRACTICE

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