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Course Overview & Navigation

A comprehensive guide to all available courses and learning paths in the Agentic AI Systems curriculum.

📚 All Courses at a Glance

### 🧠 AI Systems (Foundation Track) **Master the theoretical foundations** - **Duration**: 11 chapters - **Level**: Beginner to Intermediate - **Focus**: Theory, concepts, and principles - **Prerequisites**: Basic AI/ML knowledge Key topics: Generative AI fundamentals, agentic principles, cognitive architectures, multi-agent systems, ethics [Start Course →](AI_Systems/index.md){ .md-button .md-button--primary } --- ### ⚡ Agent Development (Implementation Track) **Build production-ready AI agents** - **Duration**: 10 chapters - **Level**: Intermediate to Advanced - **Focus**: Hands-on coding and implementation - **Prerequisites**: Python programming, basic AI concepts Key topics: LangChain, LangGraph, DSPy, debugging, deployment, state management [Start Course →](Agentic_AI_in_Action/index.md){ .md-button .md-button--primary } --- ### 🚀 Modern AI Frameworks (Cutting-Edge Track) **Explore the latest technologies** - **Duration**: 8 chapters - **Level**: Intermediate to Advanced - **Focus**: Latest frameworks and tools (2024-2025) - **Prerequisites**: Agent development experience Key topics: Pydantic AI, MCP, autonomous agents, OpenAI Swarm, enterprise platforms [Start Course →](Modern_AI_Frameworks/index.md){ .md-button .md-button--primary } --- ### 📈 AI Strategies (Leadership Track) **Lead AI transformation** - **Duration**: 17 chapters - **Level**: Advanced - **Focus**: Strategic planning and organizational change - **Prerequisites**: Business/technical leadership experience Key topics: Strategic planning, team building, change management, ROI measurement, governance [Start Course →](AI_Strategies/index.md){ .md-button .md-button--primary }

Path 1: Complete Beginner

graph LR
    A[AI Systems] --> B[Agent Development]
    B --> C[Beginner Labs]
    C --> D[Modern Frameworks]
    D --> E[Advanced Labs]
  1. AI Systems - Build theoretical foundation
  2. Agent Development - Learn practical implementation
  3. Beginner Labs - Practice with guided exercises
  4. Modern Frameworks - Explore latest tools
  5. Advanced Labs - Build complex systems

Path 2: Experienced Developer

graph LR
    A[Agent Development] --> B[Modern Frameworks]
    B --> C[Advanced Labs]
    C --> D[AI Strategies]
    D --> E[Frontier Research]
  1. Agent Development - Jump into coding
  2. Modern Frameworks - Master cutting-edge tools
  3. Advanced Labs - Build sophisticated systems
  4. AI Strategies - Scale to production
  5. Frontier Research - Explore future directions

Path 3: Technical Leader

graph LR
    A[AI Strategies] --> B[AI Systems]
    B --> C[Modern Frameworks]
    C --> D[Agent Development]
    D --> E[Enterprise Focus]
  1. AI Strategies - Strategic understanding
  2. AI Systems - Technical foundations
  3. Modern Frameworks - Technology landscape
  4. Agent Development - Implementation knowledge
  5. Enterprise Platforms - Production deployment

Path 4: Researcher/Academic

graph LR
    A[Frontier Research] --> B[Modern Frameworks]
    B --> C[AI Systems]
    C --> D[Advanced Labs]
    D --> E[Publications]
  1. Frontier Research - Latest developments
  2. Modern Frameworks - Cutting-edge tools
  3. AI Systems - Theoretical depth
  4. Advanced Labs - Research implementations
  5. Research Projects - Original contributions

🔍 Find Content by Topic

Core Technologies

Advanced Concepts

Implementation Focus

Strategic Topics

📊 Course Statistics

Course Chapters Labs Difficulty Est. Time
AI Systems 11 0 Beginner 15-20 hours
Agent Development 10 13 Intermediate 25-30 hours
Modern Frameworks 8 0 Advanced 12-15 hours
AI Strategies 17 0 Advanced 20-25 hours
Total 46 13 - 70-90 hours

🚀 Getting Started

  1. Assess Your Background: Choose a learning path based on your experience level
  2. Set Learning Goals: Decide whether you want theoretical knowledge, practical skills, or strategic understanding
  3. Use Navigation Tools: Leverage the horizontal tabs, search, and tags for easy content discovery
  4. Track Progress: The system automatically tracks your progress across all courses
  5. Practice Regularly: Use the labs to reinforce theoretical concepts

💡 Study Tips

  • Start with foundations if you're new to AI agents
  • Jump to implementation if you have AI/ML background
  • Focus on strategy if you're in a leadership role
  • Explore research if you're working on cutting-edge projects
  • Use keyboard shortcuts (Press ? for help)
  • Bookmark important pages for quick reference

Ready to begin? Choose your learning path and start your journey into agentic AI systems!