Mastering Generative AI : Google Gemini, IBM Watson & MCP

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

What you'll learn
- 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 high‑quality outputs in Gemini, including iterative refinement techniques.
- Build practical no‑code 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 real‑world scenarios where MCP adds value—without writing code.
- Plan and configure no‑code MCP‑enabled 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 workflows—from initial draft to structured review and finalization—using 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.

Requirements
- Good news: there are no prerequisites. This course is designed for complete beginners and requires no coding, data science, or AI background.
- zero experience in AI, analytics, or programming
- Looking to apply AI practically without code

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:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

Who this course is for:
- Professionals, students, creators, or managers looking to apply AI practically without code
- Beginners with zero experience in AI, analytics, or programming
- Students and career changers exploring Generative AI fundamentals and real‑world applications
- Business analysts and managers who want to turn data and prompts into clear, actionable outputs
- Creators, freelancers, and entrepreneurs looking to automate content and workflows without programming
- Team leads and non-technical stakeholders who need to evaluate AI tools and collaborate with technical teams
- Educators and trainers building AI literacy programs or integrating AI into curricula
- Anyone interested in Google Gemini, IBM Watson Analytics, and Model Context Protocol (MCP) without writing code
More Info