See the course in action
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- All content hours, with no expiry
- CodeLab in the browser, nothing to install
- AI tutor available at any hour
- AI-graded exercises
- Final exam and verifiable certificate + an optional field challenge for the Field Practice Validated distinction
Final exam: 2 attempts, 70% pass threshold. After a second failed attempt, a deferral period applies before retrying, with a $10.99 retake fee — see the Terms of Service.
Lifetime access. No deadline: finish in 12 weeks or in ten months.
Machine Learning Engineer
Build, validate and deploy predictive models with code
- Level
- Intermediate
- Duration
- 12 weeks
- Total hours
- 65 h(estimated personal workload)
- Price
- $15
The program, module by module
- Module 1
Scientific Python (NumPy, Pandas)
Load, explore and clean a dataset with NumPy and Pandas before handing it to a model.
- Module 2
Statistics for ML
Read a distribution, test a relationship and audit a dataset to interpret a model without being fooled.
- Module 3
Data Preparation and Feature Engineering
Clean, encode and create predictive features from raw logs, without ever using information dated after the reference date.
- Module 4
Supervised Models
Train, interpret and compare regression, decision tree and random forest with a metric chosen for the business.
- Module 5
Unsupervised Models
Group customers without labels, reduce a dataset to two dimensions and turn groups into usable segments.
- Module 6
Boosting with XGBoost
Explain gradient boosting, tune XGBoost methodically and choose a model and a threshold on a highly imbalanced fraud problem.
- Module 7
Deep Learning with PyTorch
Handle tensors, build a neural network, read its training and decide whether it beats the tree-based model.
- Module 8
Validation and Optimization
Validate a model without information leakage, tune its hyperparameters within a budget and honestly measure the gain obtained.
- Module 9
Deploying a Model via API
Save a model with its preprocessing, expose it through a validated API and prepare its containerization.
- Module 10
Final Project and Certification
Frame a machine learning project end to end, deliver it in a tested repository and prepare its defense on the grading grid.
- Module 11
Career Entry and Certification
CertificationPresent your machine learning projects through a defensible CV and portfolio, and practice a technical interview case study.
How long will it take me?
Duration: 65 h (estimated personal workload)
3 h/wk ≈ 22 wks
6 h/wk ≈ 11 wks
12 h/wk ≈ 6 wks
20 h/wk ≈ 4 wks
Estimates, not deadlines. Pause for a month and pick up where you left off.
CERTIFICATE VERIFIABLE ONLINE
What you deliver at the end
Present your machine learning projects through a defensible CV and portfolio, and practice a technical interview case study.
See a sample certificateHow certification works
Three levels, from simplest to most demanding. Each one only claims what it actually verifies.
Attendance certificate
Earned by: Modules followed
Proves: You followed the track — no graded assessment.
Certificate of achievement
Earned by: Full track completed on the platform, final project included, and final exam passed
Proves: Your knowledge and your final project, evaluated by the platform.
Field Practice Validated distinction
Earned by: A field challenge completed after the exam, in a real situation, with evidence
Proves: A CoursTech mentor reviewed and validated a genuine real-world application.
The distinction is added to your certificate of achievement — it never replaces it: nothing off-platform ever blocks the certificate itself.
Mentor support and validation: handled on request, within 72 business hours. If no mentor is active, the distinction stays open for 12 months as planned: no learner is penalized.
Mentor sessions: limited spots, on request. Reply within 48 h.
See a sample certificate with the distinction →What your certificate proves →