Skill · Machine learning · Frame, equip, train and reproduce machine learning models
Version a dataset outside Git and get back an exact version of it (DVC)
Track a dataset with DVC and read the pointer that dvc add writes, declare an S3 remote whose access keys stay in the environment and never in the repository, commit the pointer then push the content with dvc push, restore the dataset on a fresh clone with dvc pull and check it by its checksum, go back to an earlier version with git checkout then dvc checkout, and check with dvc status and dvc status -c that repository, local cache and remote are in sync.
Start lesson 1The Machine learning domain
- 1 lesson
- ~1 h 05 of reading
- 1 quiz
- 1 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
This skill, for you
Version a dataset outside Git and get back an exact version of it (DVC)
Computing
It unlocks · 1 skill
The path through the foundations From the most upstream foundation to this skill: what is in place, what remains to read.
Lessons
How to learn it
1 lesson, about ~1 h 05, in program order. Each lesson ends with its quiz.
The program
1 lesson · ~1 h 05
Beyond the lessons
Assess, practise, pass the proof
Reading a lesson confirms nothing. A skill is confirmed by a passed quiz, practised in a guided lab, and put to the test by a challenge or a mission; an imported lab result is not verified by the site.
Assess yourself
1 quiz
At the end of the lessons: passed at 70%, it confirms the skill.
Proof
1 practical proof
A practical proof is a challenge, a capstone or a declared lab.
Placement test
Preparing the questions…
Your answers in detail
For each question asked: your answer, the correct answer, its explanation and the page to reread.
Around it
Who requires it, who teaches it
Required by
No published job requires it yet.
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)
- 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
- 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)