Dive into Generative AI with prompt engineering, data visualization, MCP integration, and real-world automation apps.
What you will learn
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Explain the core capabilities, strengths, and ideal use cases of Google Gemini, IBM Watson Analytics, and the Model Context Protocol (MCP).
Set up and confidently navigate Google Gemini and IBM Watson interfaces, including key settings, features, and workflow areas.
Apply clear prompt engineering frameworks to consistently generate highβquality outputs in Gemini, including iterative refinement techniques.
Build practical noβcode automation workflows for tasks such as summarization, content drafting, data extraction, and productivity boosts using Gemini.
Import, explore, and prepare datasets in IBM Watson; create effective visualizations and communicate insights with clean, shareable dashboards.
Describe MCP fundamentals, architecture, and roles, and map common realβworld scenarios where MCP adds valueβwithout writing code.
Plan and configure noβcode MCPβenabled integrations to safely connect models with tools, data sources, and business workflows.
Compare Gemini vs. Watson for different tasks and select the right tool using clear decision criteria (data needs, output type, governance, and speed).
Execute iterative content generation workflowsβfrom initial draft to structured review and finalizationβusing templates and checklists.
Identify and avoid common pitfalls across prompting, data hygiene, visualization clarity, and integration setup to ensure reliable outcomes.
Design intelligent, repeatable workflows that improve efficiency, reduce manual effort, and align with business or project goals.
Scope, plan, and present a mini capstone project that combines Gemini, Watson, and MCP, including goals, process, results, and next steps.
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