ML 1.3Lesson outlineLesson 3 of 5 in Module 1
The Machine Learning Workflow
Know the steps every project goes through, and what goes wrong when one is skipped.
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Lesson outline: The Machine Learning Workflow
In this lesson9 parts
- 01A model that scores one hundred percent
- 02The ten steps of a project
- 03Framing the decision and collecting the data
- 04Splitting first, then exploring the training data
- 05The baseline, the model, and one iteration
- 06Two skipped steps, measured
- 07Deployment and monitoring
- 08The code
- 09Recap, your challenge, and what comes next
Builds on
This lesson stands on its own. These go deeper into what it uses.
Key terms: The Machine Learning Workflow
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- workflow
- The ordered steps of a machine learning project: frame, collect, split, explore, baseline, model, evaluate, iterate, deploy, monitor.
- held-aside data (reminder, ML 1.1)
- Examples set aside before any model is built, used only to score it.
- evaluation
- Scoring a trained model on data it was not trained on.
- iteration
- One change to the model, then evaluating again.
- memorisation
- Storing the training examples' answers instead of learning a rule, as model M does.
- deployment
- Running a model on new data as part of a real system.
- monitoring
- Measuring a deployed model's score on new labelled data.
- validation set (narration)
- Data kept apart for choosing between versions of a model, so the final test is used once (Module 4).
Quiz: The Machine Learning Workflow
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.