Data Science & Machine Learning
Deliver predictive models and data products using modern ML workflows with scikit-learn, PyTorch, and MLflow.
Duration
12 weeks
Format
Online only
Experience
Small cohort · high support
Next intake
Starting soon
Limited seats
Cohorts fill fast.
Secure payment via Stripe · 14-day cooling-off period.
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Book free consultationWhat you'll achieve
By the end of the 12 weeks, you will be able to:
- ✓Build, tune, and evaluate supervised and unsupervised ML models
- ✓Deploy models as REST APIs with FastAPI and Docker
- ✓Track experiments and manage models with MLflow
- ✓Apply deep learning fundamentals with PyTorch
- ✓Deliver an end-to-end ML project in your portfolio
12-week curriculum
Twelve weeks of structured live online learning. Sessions are recorded and available within 24 hours.
Student voices
Real students. Real results.
Notebooks were my comfort zone. Docker was not. Shipping a model behind a tiny API in the cohort repo felt silly until my interviewer asked 'how would you hand this to engineering?' I had an answer.
Tom A.
ML engineer · Cambridge
I kept losing experiments on my laptop. MLflow in the course felt like admin homework until I rebuilt an old project and saw every run in one place. Saved me from sending the wrong model zip twice.
Peter O.
Data scientist · Belfast
Maths intimidated me; I said so on day one. Nobody promised shortcuts. They showed the bit you need for the job, skipped the rest, and moved on. I still Google proofs, but I can ship.
Liam H.
Junior data scientist · Oxford
Frequently asked questions
Core questions that apply to every Branxl cohort. For week-by-week detail, use the curriculum section above or the syllabus download in the enrol box.
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