MODULE 1
Problem and data
prediction goals, target variable, data quality and separation of development and test sets.
Vibecoding
Vibe Coding and Machine Learning: Build Predictive Models with AI
The Vibe Coding and Machine Learning course covers assisted development of predictive models. Explore regression, classification, clustering and sales forecasting, with attention to data preparation and evaluation on examples not used to build the model.
Understand. Apply. Evaluate.
Proposed duration12 hours
Programme6 course modules
ArrangementsInformation on request
Why choose this course.
Models need comparison with simple baselines and appropriate data. The course helps verify generated code and distinguish a promising experiment from a reliable system.
THE SKILLS
What you will learn.
- Choose an approach suited to the prediction problem.
- Check separation between development and test data.
- Compare models and baselines using appropriate metrics.
- Communicate errors and limitations in prototype results.
THE PROGRAMME
A course, step by step.
MODULE 2
Regression
build an initial numerical estimate and compare it with a baseline.
MODULE 3
Classification
classes, probabilities, thresholds and error interpretation.
MODULE 4
Clustering
variable preparation, grouping and segment interpretation.
MODULE 5
Sales forecasting
time structure, seasonality, result comparisons and forecast limitations.
MODULE 6
Evaluation and interface
metrics, leakage risk, testing and displaying results in the prototype.
What the course includes
- Applied exercise described in the programme
WHO IT IS FOR
Find your starting point.
- Data analysts and data professionals
- Marketing and sales analysts
- Developers and consultants with quantitative foundations
Before you start
- Introductory statistics and data analysis knowledge.
- Basic Python or equivalent experience is recommended for checking code.
References and further reading
An example application
Prototype a comparison between a baseline and a model using demonstration data, display an error metric and explore estimates, with a note explaining when they should not be used.
YOUR QUESTIONS, ANSWERED
Questions about the course.
Does AI guarantee accurate models?
No. Data quality, experiment design and metric validation remain essential. Results on a few examples do not establish reliability on future cases.
Does the course cover model limitations?
Yes. The programme covers baselines, errors, leakage and generalization limits to identify when results do not support a decision.
What is the proposed duration?
The proposed duration is 12 hours. The school will confirm scheduling, session structure, delivery arrangements and tool requirements according to the agreed course.
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Programme and detailsYOUR NEXT STEPYour next step
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starts here.
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