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Skills · Machine learning
Machine learning
10 skills to acquire.
Start · Frame a machine learning problem: choose the learning family and place the work in the project lifecycleAll skills
- 10 skills to acquire in this domain
- 1 step in the order you learn them
- 0/10 acquired from your progress, in this browser
- 4 prerequisite domains to know before starting here
Your path
The domain's steps
In the order you learn them: each step builds on the previous one. Your status is computed in your browser, from what you have read and passed (quizzes, labs).
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Frame, equip, train and reproduce machine learning models
See the 10 skills
- 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)
- Group unlabelled data with k-means and choose the number of clusters (elbow, silhouette)
- 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)
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