Skill · Machine learning · Frame, equip, train and reproduce machine learning models
Group unlabelled data with k-means and choose the number of clusters (elbow, silhouette)
Train a scikit-learn k-means on unlabelled data (n_clusters, n_init, random_state), read the centres and the inertia, choose the number of clusters with the elbow method and the silhouette coefficient, and recognise when DBSCAN fits better, with noise and irregularly shaped groups.
Start lesson 1The Machine learning domain
- 1 lesson
- ~30 min of reading
- 1 quiz
- 0 proof lab
The path
Where it comes from, where it leads
What you need to know first, where you stand on this skill, and what it opens next. Your status is computed in your browser, from what you have read and passed.
Before · 1 prerequisite
Recommended before · 2
This skill, for you
Group unlabelled data with k-means and choose the number of clusters (elbow, silhouette)
Computing
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Lessons
How to learn it
1 lesson, about ~30 min, in program order. Each lesson ends with its quiz.
The program
1 lesson · ~30 min
Beyond the lessons
Assess, practise, pass the proof
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Assess yourself
1 quiz
At the end of the lessons: passed at 70%, it confirms the skill.
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Proof
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Neither a challenge nor a mission with a proof yet.
Placement test
Preparing the questions…
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Taught by
Same block · Frame, equip, train and reproduce machine learning models
- Frame a machine learning problem: choose the learning family and place the work in the project lifecycle
- Set up a reproducible data science workstation: a pinned conda environment and a Jupyter notebook that runs end to end
- Train a linear regression and evaluate it on a test set (scikit-learn, R², RMSE)
- Train a small neural network on CPU with PyTorch and read its decision boundary
- Version a dataset outside Git and get back an exact version of it (DVC)
- Make a training run reproducible and describe it as a parameterised pipeline replayed identically (uv, DVC)
- Track every training run on a tracking server and find the run that produced a model (MLflow)
- Register and version a model and refer to it by alias with its lineage (MLflow)
- Test data and model before promotion: schema, ranges, threshold and non-regression against the champion (pytest)