Skill Set
Skills, Track by Track
Nine tracks covering the AI/ML engineering stack end to end — each broken down to the sub-topic level and grounded in specific coursework, not generic labels. Expand a track to see the detail.
Foundations & Algorithms
- Functions, scope, and recursion
- Lists, tuples, and dictionaries
- Objects and classes
- Testing and debugging strategies
- Algorithmic complexity (Big-O)
- Search and sort algorithms
Performance Engineering
- Profiling and bottleneck analysis
- Cache-aware programming
- Parallelism and concurrency
- Vectorization
- Memory allocation behavior
- Performance measurement methodology
OOP & Advanced Python
- Classes, instances, and class variables
- classmethods and staticmethods
- Inheritance and subclassing
- Special (magic/dunder) methods
- Property decorators — getters, setters, deleters
- Decorators and decorators with arguments
- Generators
- Context managers
- *args and **kwargs
Software Design
- Software design patterns applied to Python
- Why most ML code in production is badly engineered
- Clean architecture and separation of concerns