Mastering Generative AI : Google Gemini, IBM Watson & MCP

Dive into Generative AI with prompt engineering, data visualization, MCP integration, and real-world automation apps.

Description

This no-code course provides a structured, practical introduction to three pillars of modern generative AI: Google Gemini, IBM Watson Analytics, and the Model Context Protocol (MCP). Designed for all levels, it consists of 20 concise lessons (~7 minutes each) that progress from fundamentals to real-world applications. You will learn how to set up tools, craft effective prompts, explore and visualize data, and plan integrations—without any programming.

What you’ll learn across 20 topics:

1. Introduction to Google Gemini: Understand Gemini’s capabilities, core features, and where it excels.

2. Getting started with Gemini: Simple setup steps, account options, and navigating key interfaces.

3. Prompt engineering for Gemini: Clear frameworks for writing, refining, and testing prompts that deliver consistent results.

4. Practical automation with Gemini: Time-saving workflows for drafting content, summarizing, and task optimization.

5. IBM Watson Analytics overview: Orientation to the interface, terminology, and analytics workflow.

6. Navigation and sections in Watson: How to import data, organize assets, and use dashboards efficiently.

7. Data discovery and visualization: Exploratory analysis, visual best practices, and insight communication in Watson.

8. Hands-on analytics (no code): Guided exercises using sample datasets to create charts, summaries, and shareable outputs.

9. MCP fundamentals: What the Model Context Protocol is, why it matters, and common use cases.

10. MCP principles and patterns: Core concepts, roles, and design patterns explained in plain language.

11. MCP implementation (no code): Practical, tool-based approaches to configure and use MCP-enabled integrations.

12. Standards and best practices: Governance, security basics, and emerging conventions around MCP.

13. Choosing the right tool: Clear comparison of Gemini and Watson strengths to match the right tool to each task.

14. MCP + Gemini: High-level integration scenarios to extend Gemini with tools and data safely and effectively.

15. MCP + Watson: Practical ways to enhance analytics workflows and insights using MCP-enabled connections.

16. Iterative content generation: Structured methods to draft, review, refine, and finalize AI outputs.

17. Intelligent workflows for efficiency: Building repeatable, business-friendly processes that save time and reduce errors.

18. Common mistakes to avoid: Practical guidance on prompt quality, data hygiene, and integration pitfalls.

19. Advanced use cases: Realistic scenarios across content, analytics, research, and operations—no coding required.

20. Capstone and next steps: A guided mini-project plan to combine Gemini, Watson, and MCP, plus checklists and resources.

Who this course is for:

• Professionals, students, creators, and managers looking to apply AI in content, analytics, and automation

• Anyone seeking a clear, no-code path to practical AI skills

How you’ll learn:

• Short, focused lessons with step-by-step guidance

• Repeatable templates, checklists, and workflows

• Realistic examples you can adapt to your context

Outcomes:
By the end of the course, you will be able to:

• Set up and navigate Gemini and Watson confidently

• Write effective prompts and build iterative content workflows

• Explore and visualize data to communicate insights clearly

• Understand MCP and plan practical, no-code integrations

• Select the right tool for the job and avoid common mistakes

No coding experience is required. The course emphasizes clarity, decision-making, and dependable outcomes.

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