Agile Leader Training Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Foundations of Computing & Algorithms
Estimated time: 3 hours
- Review of tools and frameworks commonly used in practice
- Hands-on exercises applying foundations of computing & algorithms techniques
- Interactive lab: Building practical solutions
- Discussion of best practices and industry standards
Module 2: Neural Networks & Deep Learning
Estimated time: 2 hours
- Case study analysis with real-world examples
- Interactive lab: Building practical solutions
- Assessment: Quiz and peer-reviewed assignment
Module 3: AI System Design & Architecture
Estimated time: 4 hours
- Hands-on exercises applying ai system design & architecture techniques
- Interactive lab: Building practical solutions
- Guided project work with instructor feedback
Module 4: Natural Language Processing
Estimated time: 1-2 hours
- Introduction to key concepts in natural language processing
- Guided project work with instructor feedback
- Interactive lab: Building practical solutions
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2-3 hours
- Introduction to key concepts in computer vision & pattern recognition
- Case study analysis with real-world examples
- Discussion of best practices and industry standards
Module 6: Deployment & Production Systems
Estimated time: 3-4 hours
- Interactive lab: Building practical solutions
- Hands-on exercises applying deployment & production systems techniques
- Assessment: Quiz and peer-reviewed assignment
Prerequisites
- Basic understanding of agile concepts
- Familiarity with fundamental computing principles
- Interest in leadership and team management in agile environments
What You'll Be Able to Do After
- Apply computational thinking to solve complex engineering problems
- Understand core AI concepts including neural networks and deep learning
- Design algorithms that scale efficiently with increasing data
- Program and evaluate transformer architectures and attention mechanisms
- Implement prompt engineering techniques for large language models