Machine Learning with Python
Build and evaluate a defensible prediction model using a synthetic business dataset.
Machine Learning with Python develops practical capability through six curriculum modules combining focused lessons, guided activities and assessed deliverables. Build and evaluate a defensible prediction model using a synthetic business dataset. The six-week outline is an estimated learning sequence, not a confirmed cohort timetable; tutors and administrators configure actual dates separately.
Learning outcomes
- Define a fictional churn prediction task.
- Build a leakage-safe feature pipeline.
- Compare baseline and candidate models.
- Analyze false positive and negative cases.
- Write a model use and limitation report.
Who it's for
Data practitioners and aspiring ML engineers
Prerequisites
Python, pandas and basic statistics
Learning tools
Python, scikit-learn and synthetic datasets
Estimated learning time
48 hours · 6 curriculum modules
Curriculum
Final project
Build and evaluate a defensible prediction model using a synthetic business dataset.
Completion & certificates
- Complete each published lesson.
- Submit every practical assignment and the final project.
- Satisfy the published assessment criteria in tutor or administrator review.
- Receive administrator completion review before certificate eligibility.
Certificate eligibility requires verified completion and administrator review. No certificate has been issued for this course.
Cohorts & class dates
No cohort dates have been confirmed. The curriculum sequence is not a scheduled class timetable.