// school · machine-learning
🧠Machine Learning
From supervised learning to deep neural networks. Build models that solve real problems, not Kaggle competitions.
Start This School// career outcomes
Where This School Takes You
// core skills
What You Will Master
// your path
Path Through This School
Phase 01
Foundation
Prove your fundamentals. Python, Git, SQL, APIs, Docker, and AI basics — calibrated to your level before you touch Machine Learning.
Phase 02
Build
Work through real Machine Learning tickets inside virtual companies. Ship features, fix bugs, and build systems under production constraints.
Phase 03
Prove
Every submission is scored across 5 dimensions. Build a verified Proof-of-Competency profile that proves you can do the work of a ML Engineer.
// your faculty
Your Faculty
Dr. Sarah Lin
Dean
Research-minded leader. Values rigor and reproducibility. Celebrates methodical thinking over clever hacks.
Priya
Lead Instructor
Research-oriented. Pushes learners to understand WHY, not just HOW. Often references foundational concepts.
David
scikit-learn, feature engineering, evaluation
Mei
CNNs, transformers, transfer learning
Alejandro
Image processing, edge deployment
Ready to start Machine Learning?
Begin with Foundation. Prove your baseline. Then build real systems.
Start Building