What Is Generative AI?
Apply generative AI tools to boost your professional workflows
Generative AI refers to machine-learning approaches that produce original content—text, images, audio, and video—by learning patterns from existing data. These systems include large language models, text-to-image generators, and GAN variants that enable practical content creation and automation across creative and business workflows.
For professionals and creators, generative AI unlocks new productivity and creative possibilities while introducing important ethical, legal, and quality considerations that must be managed; learners should expect to study model types, responsible usage, and practical workflows that translate theory into work-ready skills.
At a Glance
What Is Generative AI? is an introductory course taught by Pinar Seyhan Demirdag that explains core concepts of generative AI and practical ways to create content using modern models.
It covers model types, workflow examples, future implications, and ethical considerations for professionals applying generative AI in diverse industries.
| Level | Beginner |
| Rating | 4.7 out of 5 |
| Duration | 1+ hours |
| Languages | English |
| Certificate | Certificate of Completion |
| Access | Included with a LinkedIn Learning subscription; access while your subscription is active. Sign in to earn the certificate and access exercise files. |
| Course includes |
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| Price | Subscription-based (included with LinkedIn Learning; monthly or annual plans) |
What This Course Teaches
This course frames outcomes as practical competencies: understanding generative AI concepts, recognizing model types, and producing generative content for professional use.
By completing the course, learners should be able to explain core models, create content using current tools, apply LLM-based productivity techniques, and evaluate ethical and legal implications.
How the Course Is Structured
The course is arranged as a sequence of short, focused lessons grouped under thematic sections, and it runs for roughly one hour in total.
In total there are 27 discrete lessons spanning introductory material, model types, future implications, ethics, and applied workflows.
Curriculum overview
01 Introduction ▾
Course landing and orientation to the syllabus and goals.
02 Generative AI is a tool in service of humanity ▾
Positions generative AI in social and professional contexts and its potential benefits.
03 What’s new? ▾
Highlights recent developments that have accelerated adoption of generative techniques.
04 1. What Is Generative AI? ▾
Section header introducing foundational concepts covered in the following lessons.
05 The importance of generative AI ▾
Explains why generative AI matters across industries and creative fields.
06 How generative AI is different than other types of AI ▾
Contrasts generative approaches with discriminative or predictive AI techniques.
07 How generative AI works ▾
A concise walkthrough of model training, sampling, and common architectures at a conceptual level.
08 Creating your own content ▾
Practical demonstration of simple content-generation workflows using available tools.
09 2. Main Models ▾
Section header introducing model families and tool categories covered next.
10 The most famous tools for generative AI ▾
Surveys well-known generative tools and distinguishes their typical use cases.
11 Natural language models ▾
Overview of large language models and how they generate and transform text.
12 Text to image applications ▾
Introduces prompt-driven image generation and examples of visual output creation.
13 Generative Adversarial Networks (GANs) ▾
Explains the GAN framework and common applications such as image synthesis.
14 VAE and Anomaly Detection ▾
Covers variational autoencoders and their use in detecting anomalies in data.
15 3. The Future of AI ▾
Section header that frames forward-looking discussions on impact and trends.
16 Future predictions ▾
Considers projected technological advances and likely directions for generative models.
17 The future of jobs ▾
Discusses how roles and workflows may shift as generative AI becomes more common.
18 4. Ethics and Responsibility ▾
Section header introducing ethical considerations and governance topics.
19 Moral and executive skill set required to work with Gen — AI ▾
Outlines the professional judgement and organizational skills needed for responsible use.
20 Caution when working with Gen AI ▾
Covers practical risks and points of caution when deploying generative systems.
21 5. Working with Generative AI ▾
Section header that moves from theory to practical integration and tools.
22 Productivity enhancements in large language models (LLMs) through APIs and real-time interactions ▾
Demonstrates how LLMs and APIs can be integrated to automate and speed common tasks.
23 From technical demos to professional productions ▾
Shows examples of scaling prototype demos into production-quality outputs for work use.
24 Wider adoption of generative AI ▾
Explores barriers and drivers for broader adoption across sectors and organizations.
25 Legal frameworks and intellectual property in the age of AI ▾
Addresses legal and IP considerations relevant to AI-generated content and workflows.
26 Conclusion ▾
Wraps up key points and suggests practical next steps for continued learning.
27 Next steps ▾
Provides recommendations for further study and practical application after the course.
Audience & Requirements
What Is Generative AI? targets working professionals and creators across sectors — for example, people in film, marketing, healthcare, automotive, and real estate — who need a concise, practical introduction to how generative AI can be applied in their roles. The course is pitched at beginners and assumes no special prerequisites while focusing on applied, industry-relevant understanding rather than deep technical training.
- Marketing and creative professionals looking to integrate generative tools into content workflows.
- Product managers and business leaders assessing AI opportunities and strategy.
- Content creators, VFX artists, and filmmakers exploring generative production techniques.
- Industry professionals (healthcare, automotive, real estate) seeking practical familiarity with generative AI applications.
- No special prerequisites — the course is designed for beginners.
- A LinkedIn Learning account and sign-in to view the course, earn the certificate, and access exercise files.
Final Verdict
This course is a concise, practical primer suited to professionals and creators who need a clear, applied introduction to generative AI rather than deep technical training. It is best used as a foundation for immediate workplace application and follow-up learning, not as a route to technical mastery.
Given its strong platform rating and wide learner uptake, the course represents good value for anyone with access to the hosting platform; the included certificate and bundled exercise files increase its practical utility. Prospective learners who do not already subscribe should weigh the short runtime and introductory scope against the subscription model when deciding whether to enroll.
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