Mastering Generative AI : Google Gemini, IBM Watson & MCP

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 automationAnyone seeking a clear, no-code path to practical AI skillsHow you’ll learn:Short, focused lessons with step-by-step guidanceRepeatable templates, checklists, and workflowsRealistic examples you can adapt to your contextOutcomes:By the end of the course, you will be able to:Set up and navigate Gemini and Watson confidentlyWrite effective prompts and build iterative content workflowsExplore and visualize data to communicate insights clearlyUnderstand MCP and plan practical, no-code integrationsSelect the right tool for the job and avoid common mistakesNo coding experience is required. The course emphasizes clarity, decision-making, and dependable outcomes.