Google Gemini for Marketing: Strategy, Content, Productivity

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This lesson analyzes real market and competitive cases through structured discussion, focusing on how to read markets, identify competitive dynamics, and translate insights into strategic decisions.

This lesson introduces the Jobs-to-be-Done framework to explain customer motivation beyond demographics, helping identify the real reasons customers choose or reject products.

This lesson deepens JTBD analysis using supporting documents, showing how to validate assumptions, refine customer jobs, and connect insights to strategy and messaging.

Learn how to use Udemy's AI Assistant for streamlining your learning.

This lesson explores situations where brands lose relevance, analyzing the structural reasons behind decline and the strategic signals that indicate the need for revitalization.

This lesson focuses on rebuilding positioning after revitalization, clarifying target choices, competitive differentiation, and the trade-offs required to restore brand clarity.

This lesson explains how strategic messages are translated into consistent, everyday content signals that reinforce positioning across all customer touchpoints.

This lesson examines how media decisions operationalize strategy, aligning channels, budgets, and formats with real market behavior and constraints.

This lesson frames marketing as a coherent decision system, showing how aligned strategy, data, and processes reduce randomness and make execution resilient over time.

This assignment uses AI to help you clarify your market, customer Jobs, and strategic choices so marketing decisions become consistent instead of chaotic.

In this lesson, you'll get your own AI companion: the book "AI for Business".

Use Gemini to streamline content creation across platforms.

Create visuals, illustrations, and concepts using AI-powered image generation.

Explore how Gemini can help generate melodies, lyrics, and creative musical ideas.

Generate engaging posts, captions, and content strategies for social platforms.

Transform raw ideas into actionable plans and completed outcomes with AI guidance.

Develop an effective communication strategy using AI-driven insights to enhance brand messaging.

In this lesson, learn how to use Gemini for social media posting.

Master the art of crafting compelling CTAs that encourage user action and boost conversions.

Automate the process of creating a comprehensive FAQ list to improve customer support and user experience.

This additional module helps you understand the basic capabilities of Google Gemini and understand how to use the tool in the daily work of a marketer.

Learn how to use Gemini in Google Docs for smarter writing assistance.

Discover how Gemini enhances email drafting in Gmail.

Explore how Gemini helps with YouTube content research and insights.

Generate high-quality text efficiently with Gemini.

Conduct voice-based keyword research with Gemini.

Use Gemini to organize your schedule, prioritize tasks, and plan your week efficiently.

Learn how to turn ideas and goals into clear, actionable task lists using AI.

Transform raw ideas into actionable plans and completed outcomes with AI guidance.

Design repeatable processes that streamline tasks using Gemini as a co-pilot.

Reduce manual work by identifying and automating routine activities with AI.

This lesson shows how Gemini can gather and summarize real-time information to support project planning decisions.

Learn how to create AI-powered product guides and interactive walkthroughs with Guideless. Record workflows, automatically generate step-by-step documentation, edit content, and publish professional guides for onboarding, training, and customer education.

We’ll explain the difference between narrow AI (task-specific), general AI (human-level), and superintelligence — and where today’s models fit on that scale.

You’ll learn how models actually “think” — not with logic or visuals, but through tokens, probabilities, and associations — and how that affects their responses.

We’ll break down parameters like temperature, max tokens, and top-p — and how they impact the model’s tone, style, and predictability.

This lesson shows how to set roles, formats, constraints, and styles — even if you don’t write code. It’s about “programming with words.”

We’ll introduce PromptOps as a systematic approach to working with prompts — treating them as a repeatable process rather than random one-off requests.

You’ll learn that not all prompts are equal — some are disposable, some reusable, some dynamic with variables, and some become the core "brain" of an AI agent.

Understand how memory works in large language models — what information they retain, how they use context, and where their limits are.

Explore how modern AI systems now include built-in memory to personalize and adapt to your needs.

This lesson explores how companies and creative teams integrate PromptOps into their daily workflows — automating research, content creation, and analysis through structured prompt systems.

In this lesson, we’ll explore the difference between precise instruction (Prompt Engineering) and creative dialogue with the model (Vibe Coding) — and why both are essential for professional AI work.

This lesson explains the basics of prompting — how a model thinks, why the precision of your request determines the quality of the response, and how to build a systematic workflow instead of random interactions.

This lesson teaches how to guide a model using examples: you show it a few samples of what you want, and it learns to imitate the structure, tone, and logic to generate consistent results.

This lesson demonstrates how to make the model “think out loud” — explaining its reasoning step by step before giving a final answer, improving accuracy and transparency.

This lesson reveals a method where the model generates several reasoning paths and selects the most consistent one — a kind of “collective intelligence” inside the model.

This lesson shows how a model can generate intermediate knowledge on its own when context is missing — and then use that knowledge to produce a more accurate answer.

This lesson teaches how to break down a complex task into a sequence of smaller prompts that build upon each other, forming a logical workflow.

This lesson introduces a reasoning approach where the model explores multiple thought paths in parallel, pruning weaker ideas and keeping the strongest solutions.

This lesson explains how to combine generation with retrieval — the model doesn’t invent data but searches for factual information in external sources and integrates it into its response.

This lesson shows how a model can create or optimize prompts by itself, testing multiple variations and selecting the most effective one.

This lesson introduces a dynamic prompting technique where prompts evolve during the conversation based on intermediate results, improving output quality in real time.

This lesson explains how to guide the model’s reasoning direction — through subtle hints that orient it toward a particular type of logic, creativity, or emotional tone.

This lesson demonstrates an approach where the model both reasons and acts — analyzing, verifying, taking actions, and adjusting its behavior like a human problem-solver.

This lesson demonstrates how models can not only generate text but also automatically use tools — calculators, APIs, or databases — as part of their reasoning process.

This lesson explores how language models can combine natural language with code — generating not just explanations, but executable program fragments that solve tasks automatically.

This lesson teaches models to “learn from themselves”: they review their previous answers, evaluate mistakes, and generate improved responses based on reflection.

This lesson shows how the logic of Chain-of-Thought applies not only to text but also to images, audio, and video — creating true multimodal reasoning.

This lesson explains how models can think non-linearly, using graph structures of nodes and connections between ideas to represent complex systems of knowledge.

This lesson explores the concept of “prompts that create prompts” — teaching the model to design its own instructions for different tasks, becoming a true co-engineer in your workflow.

This lesson explains the basics of prompting — how a model thinks, why the precision of your request determines the quality of the response, and how to build a systematic workflow instead of random interactions.

Understand the core principles for using AI responsibly, ethically, and safely in professional environments.

Learn how to respect intellectual property, give proper credit, and ensure originality when using AI-generated content.

Identify and reduce biases in AI systems to promote fairness, equity, and inclusion in outcomes.

Discover best practices for protecting personal and confidential data when interacting with AI tools.

Develop skills to verify and validate AI-generated information to ensure accuracy and reliability.

Explore global AI regulations and understand how policies like GDPR affect AI usage and compliance.

Learn strategies to balance AI automation with critical human judgment and decision-making.

Uncover how AI can supercharge your professional development and open new career opportunities in the corporate world.

Stay on the cutting edge with the latest tools, trends, and techniques to maximize your GenAI and ChatGPT skills.

In this video, you will find out how to get a certificate after completing the course.

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