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Professional Certificate
Machine Learning Engineering
From a dataset to a model that runs reliably in production.
- Length
- 6 or 12 months
- Level
- Intermediate
- Weekly
- 8 to 12 hours
- Format
- Live cohort, Hybrid, Self-paced
- Modules
- 8 to 13
- Next intake
- To be announced
What you'll learn
A rigorous grounding in classical machine learning with an engineering mindset: reproducible pipelines, honest evaluation and models that keep working after launch. The 12-month option adds deep learning, ranking systems and deployment.
By the end, you will be able to
- Frame business problems as supervised or unsupervised learning tasks
- Build leak-free feature pipelines with scikit-learn
- Choose metrics that match the cost of errors
- Tune and compare models with cross-validation
- Explain model predictions to non-technical stakeholders
- Package a model as a tested, versioned service
Curriculum
Switch between the 6- and 12-month options to see how the program expands. Every module includes a hands-on lab.
8 modules, 24 weeks, about 192 to 288 study hours
Core modulesCapstone
Topics
- Vectorised computation
- pandas for tabular data
- Notebooks vs modules
- Reproducible environments
Lab
Refactor a notebook into a tested Python package
Estimated effort
24 to 36 hours over 3 weeks
Skills you'll build
- Supervised learningAdvanced
- Feature engineeringAdvanced
- Model evaluationAdvanced
- Gradient boostingProficient
- Deep learningProficient
- Model deploymentProficient
Tools and technologies
- Python
- NumPy
- pandas
- scikit-learn
- XGBoost
- PyTorch
- MLflow
- FastAPI
- Docker
Product names are trademarks of their owners, listed only to describe what we teach. No endorsement is implied.
Portfolio projects
Representative projects from the curriculum. Your capstone is scoped with a mentor around a problem you choose.
Churn prediction with cost-aware thresholds
Predict cancellations and pick a decision threshold from the real cost of outreach.
scikit-learn, Calibration
Customer segmentation
Cluster behaviour data and validate segments against business outcomes.
Clustering, PCA
12-month option
Deployed recommender
Serve a ranking model behind an API and track drift.
PyTorch, MLflow, FastAPI
How it's delivered
- Live cohort
- Scheduled live classes each week with your cohort, recorded for different time zones, plus self-paced lessons and labs.
- Hybrid
- Self-paced lessons with fortnightly live workshops and mentor sessions.
- Self-paced
- Study on your own schedule with deadlines for assessed work and booked mentor office hours.
Instructors
Instructor and mentor profiles for this program are published once each person's background has been verified. We don't show placeholder faces or borrowed credentials.
Admissions
- 1Submit an online application
- 2Readiness review by admissions
- 3Decision by email
- 4Confirm your place and start onboarding
Tuition
- 6-month optionPublished before intake
- 12-month optionPublished before intake
Payment options vary by country and are shown at checkout.
Tuition and payment optionsCertificate
Complete every required module, pass the assessments and the capstone review to earn the Professional Certificate.
Each certificate has a unique ID and QR code that anyone can check on our verification page.
This is a professional credential issued by Zyvora Academy, not an academic degree.
Where it can lead
Roles this program builds toward. Outcomes depend on your background, effort and local job market; we don't guarantee employment.
Machine Learning Engineer
Turns data into models and models into dependable services.
Role detailsData Scientist
Answers questions and informs decisions with statistics and models.
Role details
Program questions
Syllabus
Get the full syllabus
We'll email the detailed syllabus, and the fee sheet as soon as tuition for the next intake is published.
Advisor
Speak to an advisor
Ask about fit, prerequisites or which length suits you. An advisor replies by email to arrange a call.
