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Google Gemini Master Course
A complete 156-lesson journey from absolute beginner to professional platform engineer covering every Gemini surface: gemini.google.com, Google AI Studio, Vertex AI, the Gemini API, function calling, grounding with Google Search, RAG, and beyond.
What This Course Is
Google Gemini is Google's most capable AI model family and assistant platform. It spans multiple surfaces: the consumer-facing gemini.google.com chat, Google AI Studio for developers, Vertex AI for enterprise, the Gemini API for programmatic access, and integrations across Google Workspace. Gemini 2.5 Pro and Flash represent the latest generation with multimodal capabilities, long context windows, and deep Google ecosystem integration.
This course takes you from "I just tried gemini.google.com" all the way to "I architect production platforms powered by Gemini." Every lesson explains why the topic matters, walks through how it works, gives a real-world example, surfaces common mistakes, and ends with an exercise plus an optional professional challenge.
The curriculum tracks Gemini's current state as of August 2026: Gemini 2.5 Pro and Flash models, Google AI Studio, Vertex AI, function calling, grounding with Google Search, RAG with vector databases, and the Gemini API. Where product behavior is version-sensitive, lessons carry a Last verified: YYYY-MM-DD stamp.
Who Should Take This Course
First-time AI users who want a structured on-ramp to Google's AI ecosystem.
Engineers using Gemini API or Google AI Studio who want measurable productivity gains.
Testers building automation, generating test data, debugging failing suites with Gemini.
Decision-makers concerned with code quality, security, governance, Google Cloud rollout.
Engineers building internal tooling on Gemini API, Vertex AI, function calling, RAG.
Bloggers, marketers, and authors who want Gemini as a creative partner.
What You Will Learn
By the end of the course, you will be able to:
- Explain what Google Gemini is across all its surfaces.
- Configure Gemini effectively in gemini.google.com, AI Studio, and Vertex AI.
- Prompt effectively using role, specificity, few-shot, output schemas, and chain-of-thought.
- Verify Gemini output through structured review checklists and self-checking prompts.
- Build reusable workflows with prompt templates, Projects, and Google AI Studio.
- Use the Gemini API for messages, streaming, function calling, and grounding.
- Build RAG systems with Gemini and vector databases.
- Evaluate Gemini output at scale with golden suites and regression tracking.
- Govern Gemini usage in an organization with policies, audit, and cost control.
- Architect production systems that use Gemini as a programmable AI layer.
Prerequisites
| Phase | Prerequisites |
|---|---|
| Beginner | A working computer, a Google account, basic familiarity with any text editor. |
| Intermediate | Comfortable with the web. Some lessons require JS/TS or Python and basic terminal use. |
| Professional | Git fluency, CI/CD basics, HTTP/REST fundamentals, comfort reading API docs. Some lessons require Python or TypeScript. |
Beginner to Professional Roadmap
| Phase | Theme | Arcs | Projects |
|---|---|---|---|
| Phase 1 Beginner | From zero to confidently using Gemini every day. | 8 arcs. | 5 |
| Phase 2 Intermediate | From user to operator building repeatable workflows. | 8 arcs. | 8 |
| Phase 3 Professional | From operator to platform engineer productionizing. | 8 arcs. | 10 |
Post Schedule
This course is published as 2 posts per day starting October 1, 2026:
| Date | Posts | Time (UTC) |
|---|---|---|
| Oct 1, 2026 | Landing + BE-01 | 10:00 + 15:00 |
| Oct 2, 2026 | BE-02 + BE-03 | 10:00 + 15:00 |
| ... | ... | ... |
| ~Dec 18, 2026 | PR-52 (final) | 10:00 |
Total: 157 posts over ~79 days.
SEO Optimization
Every post in this course is 100% SEO optimized with:
- JSON-LD structured data (schema.org Article, FAQPage, BreadcrumbList)
- Open Graph meta tags for Facebook and LinkedIn sharing
- Twitter Card meta tags for rich Twitter/X previews
- Canonical URLs to prevent duplicate content penalties
- Semantic HTML5 with itemprop microdata
- Optimized meta descriptions (under 160 characters)
- Targeted primary and secondary keywords per post
- Search intent matching (informational, transactional, commercial)
- Internal linking with prev/next/related navigation
- Mobile-responsive white theme for Core Web Vitals
Frequently Asked Questions
Is this course free?
Do I need a paid Gemini plan?
Which Gemini model should I use?
How often is the course updated?
Why white background?
Official Resources
- gemini.google.com Consumer Gemini
- aistudio.google.com Google AI Studio
- ai.google.dev Gemini API docs
- cloud.google.com/vertex-ai Vertex AI
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