Course overview
The Certified AI Practitioner course equips professionals with the skills to use e AI in a modern and effective way. Participants will learn how to apply AI for content creation, data analysis, workflow automation, and machine learning without needing coding or technical expertise. The course will change the way participants use of AI to be more productive and efficient at work place, save time by automating repetitive tasks, and improve decision-making in daily professional tasks.
Target audience
This course is intended for professionals without technical backgrounds who want to adopt AI tools in their daily work. Ideal participants are from business unit departments such as HR, Finance, Accounting, Customer service, Sales and Marketing and management.
Course objectives
- Apply prompt engineering techniques to improve AI-generated outputs.
- Compare different Generative AI platforms and their workplace uses.
- Create content using AI tools for audio, video, documents summarization and creation, images, and presentations.
- Conduct business data analysis using AI-driven approaches.
- Build simple machine learning workflows with no-code and low-code platforms.
- Explore the potential of Agentic AI for task execution.
- Utilize AI automation tools to streamline professional processes.
Target competencies
Course methodology
The course combines conceptual insights with hands-on practice using AI applications. Each building block is explored through guided demonstrations, real workplace examples, and structured exercises. Learning is reinforced with daily tasks and a final assessment for certification. AI Tools to be Used: ChatGPT, Copilot, Gemini, Excel, Python, KNIME, and other GenAI applications.
Course outline
PROMPT ENGINEERING
- Understanding the role of prompts in Generative AI
- Structuring instructions for accuracy and relevance
- Iterating and refining prompts for better outcomes
- Applying prompt strategies across multiple tools
DIFFERENCE BETWEEN GPTS
- ChatGPT, Gemini, and Copilot comparison
- Understanding unique strengths and limitations of each
- Platform selection for workplace tasks
- Leveraging multiple tools for improved outcomes
AI TOOLS FOR CONTENT CREATION
- Presentations design using AI platforms
- Images production for professional communication
- Videos creation with AI-driven applications
- Audio and written content generation
AI TOOLS FOR DATA ANALYSIS
- Data Preparation for Analysis
- Data Analysis and insight
- Report creation
- Data Visualization and communication
LOW-CODE/NO-CODE MACHINE LEARNING
- Clustering and segmentation
- Classification for predictive outcomes
- Market basket analysis with visual tools
- Text sentiment analysis for insights and decisions
AGENTIC AI
- Concept of autonomous AI agents
- Tasks execution agents
- Agentic AI for research and workflow assistance
- Tools for API creation
MODEL CONTEXT PROTOCOL (MCP)
- Structure and purpose of MCP
- Integration of AI models with enterprise data.
- Security, governance and controlled access.
- Application using MCP in analytics, reporting, and automation.
AI TOOLS FOR AUTOMATION
- Task identification for automation
- Automation tools utilization
- Custom automation workflow
- Automation process implementation
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