Machine Learning Foundations and Applications

AT A GLANCE

Is this programme for you?

Built for ml engineers, data scientists, analysts. AI helps you move faster; every output is checked against references, runnable examples or a human review before you rely on it.

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Who it's for: ML engineers, data scientists, analysts
IT domain: Data Science and Machine Learning (Intermediate to advanced)
Prerequisites: Mathematics Statistics and Computing; Programming Fundamentals
How AI is used: Core AI curriculum
Reviewed output: Evaluated prediction service using sample data
How work is checked: Check against references, runnable examples or domain review; record uncertain outputs
Trainer: Trainer to assign
Status: Planned · dates and fees not yet set

CORE CONCEPTS

10 concepts, each with a practice brief.

Each concept ends in evidence you can show. Briefs are starting points and are reviewed by the trainer before publication.

01
Exploratory data analysis
Run a controlled example of Exploratory data analysis for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-01
02
Feature engineering
Run a controlled example of Feature engineering for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-02
03
Train validation and test splits
Run a controlled example of Train validation and test splits for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-03
04
Supervised learning
Run a controlled example of Supervised learning for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-04
05
Unsupervised learning
Run a controlled example of Unsupervised learning for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-05
06
Classification and regression metrics
Run a controlled example of Classification and regression metrics for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-06
07
Overfitting and regularization
Run a controlled example of Overfitting and regularization for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-07
08
Data leakage prevention
Run a controlled example of Data leakage prevention for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-08
09
Model serving
Run a controlled example of Model serving for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-09
10
Model drift and retraining
Run a controlled example of Model drift and retraining for Evaluated prediction service using sample data; record assumptions, baseline and measured results.
ExperimentIntermediate to advancedEvidence: Experiment notebook and comparisonIWA-C29-10