Applied AI & Agentic AI for Professionals

Practical AI & Agentic Workflow Skills

Learn how to prototype AI systems using Python notebooks, embeddings, ChromaDB, RAG, prompt engineering, ReAct, workflow orchestration, and evaluation.

Applied AI & Agentic AI for Professionals is a practical course designed for learners who want to understand how modern AI systems are built and applied in real workflows. Learners build toward a capstone: a domain-specific AI assistant with retrieval and one workflow or agentic component.

Subtitle: Build practical AI workflows with Python notebooks, embeddings, ChromaDB, RAG, ReAct, and agentic workflow design.

Target Learners

Business analysts and data analysts
Retail, operations, and supply-chain professionals
Healthcare and research-support professionals
Software, product, and project teams
Cloud, infrastructure, and IT support professionals
HR, compliance, and training professionals
Consultants and freelancers
Professionals using AI tools who want system-level understanding

Prerequisites

Required

Basic computer skills
Comfort with web-based tools
Willingness to learn through guided notebooks
Google account for Google Colab

Helpful but not required

Basic Python exposure
Familiarity with spreadsheets, business data, and workflows
Prior use of ChatGPT, Copilot, or similar tools

Not required

Advanced mathematics
Prior machine learning experience
Full-stack development experience
Cloud engineering background

Modules

Python, Colab & Hugging Face Setup
Embeddings & Semantic Search
Chunking & ChromaDB
Retrieval Techniques
RAG & Prompt Engineering
ReAct & Agentic Workflows
Workflow Orchestration
Deployment & Demo Packaging
Evaluation & Capstone Polish
Final Presentations & Feedback

Learning Outcomes

Explain embeddings, chunking, vector databases, retrieval, and RAG
Build applied AI prototypes in Python notebooks
Use ChromaDB for searchable knowledge
Design grounded prompts and retrieval pipelines
Create a basic ReAct-style/tool-using workflow
Evaluate outputs for grounding, safety, and business value
Present a capstone AI assistant prototype or business-ready proposal

Capstone

Design and demonstrate a domain-specific AI assistant using embeddings, retrieval, RAG, and one workflow or agentic component.

Testimonials from the first cohort will be added after course completion.