YOUR LEARNING TRACK
AI / ML Engineer.
Build an understanding of how models learn and how to evaluate and operate them. This track will connect foundations with practical ML systems.
3 subjectsSelf-pacedFree accessCurriculum preview · Lessons coming soon
EXPLORE THE SUBJECTS
Choose what you want to learn.
Explore the planned subjects and their focus areas. Lessons will be added here as they are published.
PART 01Coming soon
Math & ML Foundations
Understand the ideas behind learning from data.
- Linear algebra & probability
- Optimization & loss functions
- Supervised & unsupervised learning
- Feature engineering & data preparation
4 planned focus areasView outline
PART 02Coming soon
Model Training & Evaluation
Learn to train models and assess where they work or fail.
- Training, validation & leakage
- Generalization & regularization
- Evaluation metrics & error analysis
- Neural networks & representation learning
4 planned focus areasView outline
PART 03Coming soon
ML Systems & Deployment
Move from a model experiment to a maintainable service.
- Serving & inference pipelines
- Model and data versioning
- Monitoring, drift & feedback
- Retrieval, generative models & evaluation
4 planned focus areasView outline
A preview of what’s ahead.
The focus areas describe the proposed curriculum. Full lessons, examples, and practice will be added as the track develops.