Agentic AI Course
Master the art of creating intelligent autonomous agents that can reason, plan, and act in complex environments
Curriculum
- Python Basics (Variables, Control Flow, Functions)
- Data types and operators
- Conditional statements and loops
- Functions and modules
- OOPS concepts
- File handling
- Regular expressions
- EDA (Exploratory Data Analysis)
- What is an LLM (Large Language Model)
- Tokenization concepts
- Prompt engineering basics
- Few-shot prompting techniques
- Chain-of-thought reasoning
- AI vs Agentic AI comparison
- Autonomous agents architecture
- Human-in-the-loop systems
- Single agent systems design
- Multi-agent systems coordination
- Ethics & responsible AI practices
- Industry use cases and applications
- Agent architecture layers
- Perception module design
- Reasoning engine implementation
- Memory module (short & long-term)
- Action module and execution
- Reflection pattern techniques
- Planning pattern strategies
- ReAct (Reasoning + Acting) pattern
- Multi-agent design patterns
- Document loaders and processing
- Text splitting techniques
- Embeddings concepts and applications
- Vector database fundamentals
- Prompt templates and management
- Chains and sequential processing
- Memory handling and persistence
- State management in agents
- State schema design
- State reducers implementation
- Short-term memory systems
- Long-term memory integration
- Human-in-the-loop workflows
- Agent flow control and coordination
- Traditional RAG vs Agentic RAG
- Chunking strategies optimization
- Indexing techniques for agents
- Retrieval optimization methods
- Reranking algorithms
- Context filtering and management
- Streamlit / FastAPI for UI
- Deploying Agentic AI apps to cloud
- Vector Databases (FAISS, ChromaDB)
- RAG (Retrieval-Augmented Generation) systems
- Agent monitoring and logging
- Hands-on Mini Projects (Every 2-3 Modules)
- Capstone Project: Build Your Own AI Agent
- Mock Interviews & Career Support
Course Details
Tools & Technologies
Prerequisites
- Basic computer knowledge
- Logical thinking ability
- Interest in Artificial Intelligence
- No coding background required
Final Outcome
By the end of this program, students will be able to:
Build Intelligent AI Agents
Create autonomous agents that can perceive, reason, and act in complex environments using advanced AI techniques.
Design Scalable Architectures
Design and implement scalable agent architectures with proper state management and memory systems.
Implement Memory Systems
Build sophisticated short-term and long-term memory systems for persistent agent knowledge.
Create RAG-based Assistants
Develop Retrieval-Augmented Generation assistants that can access and use external knowledge bases.
Multi-Agent Workflows
Orchestrate multi-agent systems where specialized agents collaborate to solve complex problems.
Real-World Applications
Apply Agentic AI in practical business scenarios including automation, customer service, and decision support.
Who Should Join
Fresh Graduates
Looking to build cutting-edge AI skills for future-proof careers
Career Switchers
Transitioning to AI from other fields with comprehensive training
IT Professionals
Upgrading skills to work on autonomous AI systems and agents
Non-IT Professionals
Business analysts, managers seeking AI automation skills
AI Enthusiasts
Anyone interested in building the next generation of autonomous AI
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