- Free
- Beginner
Machine Learning Crash Course
A compact ML foundation with videos, visualisations and exercises, refreshed for recent AI advances.
Open course : Machine Learning Crash Course. Opens the provider site in a new tab
Engineers / Intermediate
ML engineering and MLOps
Machine learning foundations, then the practice of getting a model into production and keeping it there with MLflow, gates and monitoring.
Who it is for
Engineers who write Python and want to move from notebooks to models that run in production.
- Steps
- 8
- Level
- Intermediate
What you can do afterwards
- Explain training, evaluation and the usual failure modes
- Track, register and promote a model with MLflow
- Gate a release on measured quality
The path, step by step
Course
Course
- Coursera (DeepLearning.AI and Stanford)
- Free with an account
- Beginner
- about 95 h
Machine Learning Specialization
The classic machine learning foundation for engineers moving into ML. You can audit it for free; a certificate needs a paid subscription.
Open course : Machine Learning Specialization. Opens the provider site in a new tab
Course
- fast.ai
- Free
- Intermediate
Practical Deep Learning for Coders (2022 part 1)
A free, practical deep learning course from the 2022 edition. Check that the content still fits before you rely on it.
Open course : Practical Deep Learning for Coders (2022 part 1). Opens the provider site in a new tab
Certify
- Databricks
- Paid exam
- Intermediate
Databricks Certified Machine Learning Associate
Entry credential for feature engineering, model training and MLflow.
Build
Track, register and gate a model
Goal. Train a small model, track every run in MLflow, register the best one and promote it only when it beats the current model on a fixed test set.
What to do
- Pick a tabular dataset and split off a test set you never touch while tuning.
- Log parameters, metrics and the model for each run.
- Register the best run and write a script that promotes it only if it beats the current one.
- Add a drift check that compares live inputs with the training data.
What good looks like. Any model in production can be traced to a run, a dataset and a test result.
Read
Posts to read
Two examples of moving training from a notebook to a governed, gated workflow.
Check
AI pilot readiness checklist
Check that data, owners and success measures are in place before a pilot.
Certify
- Databricks
- Paid exam
- Advanced
Databricks Certified Machine Learning Professional
The advanced MLOps credential: deployment, monitoring and MLflow at scale.
Course details come from each provider. Check the provider page for the latest.
Next path
- Engineers
- Intermediate
LLM apps and RAG
Build an application on a language model: open-model foundations, retrieval, the Claude API and a test set that tells you if it works.
View the pathLearn this with your team
We run workshops for company teams and for students, tailored to your stack.
Learn this with your team
Next step
Want your team to learn this together?
We give workshops to company teams and students, on-site or online. Tell us who the participants are and what they should be able to do afterwards.
Accepting projects for Q4 2026