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Generative AI courses

1067 Courses

California Institute of Technology Udemy

GenAI and Cybersecurity – Frameworks and Best Practices 2025

Driving Enterprise GenAI Adoption: Tools, Frameworks, and Real-World Case Studies from Industry Leaders
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California Institute of Technology Google Cloud Skills Boost

Gen AI Apps: Transform Your Work

Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google's gen AI applications, such as Gemini for Workspace and NotebookLM. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.
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California Institute of Technology Coursera

Gen AI: Unlock Foundational Concepts

Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leaders learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google.
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California Institute of Technology Coursera

Generative AI for Web Development

This specialization gives you hands-on experience using generative AI tools to both streamline your development workflow and build smarter web applications. You’ll discover how to work with the Cursor AI code editor, write effective prompts, and integrate intelligent features using the Claude AI model. From writing and debugging code to building ap.
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California Institute of Technology Udemy

The Complete Generative AI and Python Programming Bootcamp

Master New Skills for Your Future: Python, Generative AI, LangChain, LangGraph, RAG, Prompt Engineering, OpenAI + Gemini
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California Institute of Technology edX

Azure AI Fundamentals: Build & Deploy AI

This course offers a comprehensive introduction to artificial intelligence (AI) concepts and the array of Azure services available for developing AI solutions. Designed for individuals with both technical and non-technical backgrounds, this course requires no prior experience in data science or software engineering. It is ideal for professionals l.
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California Institute of Technology Google Cloud Skills Boost

Gen AI: Unlock Foundational Concepts

Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google.
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California Institute of Technology Google Cloud Skills Boost

Gen AI: Navigate the Landscape

Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the.
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California Institute of Technology Google Cloud Skills Boost

Gen AI: Beyond the Chatbot

Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the.
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California Institute of Technology Hanyang University

AI for Professionals: Ethics, Responsibility and Best Practices

From helping spark creative ideas to improving efficiency, GenAI is revolutionising the way we live and work. Like many new developments in technology, however, it's also opened a Pandaora's box of ethical challenges that must be considered. AI bias, inaccruacy and plagarism have caused many people to be causing about embracing the potential benef.
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A generative ai course is a fast-growing field of machine learning that can create new content, translate languages, write different types of creative content, and answer your questions in an informative way. It has great potential to revolutionize the way we create and use products.

A generative ai course refers to any artificial intelligence model that generates new data, information, or documents.

For example, many companies record their meetings, both live and virtual. Here are a few ways generative AI could transform these recordings:

And this is only a small part of all processes.

Generative AI Model Examples

There are a number of products using generative ai courses already available on the market – we'll give you a few examples below. The underlying principle of the generative ai courses at AI Eeducation varies depending on the specific model or algorithm used, but some common approaches include:

  1. Variational Autoencoders (VAEs) are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate a variety of outputs.

  2. Generative Adversarial Networks (GaN): GaNs consist of two neural networks, a generator and a discriminator, that are trained simultaneously through adversarial learning. The generator creates new data, and the discriminator evaluates how well the generated data matches the real data. The competition between the two networks causes the generator to improve over time in producing realistic outputs.

  3. Recurrent Neural Networks (RNNS) and Long Short-Term Memory (LSTM): These types of neural networks are often used to generate sequences such as text or music. RNNS and LSTM have memory that allows them to process a series of events over time, making them suitable for tasks where the order of elements is important.

  4. Transformer models: Transformer models, especially those with attention mechanisms, are very successful in various generative tasks. They can remember long-term dependencies and relationships in data, making them effective for tasks such as language translation and text generation.

  5. Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. Although they are primarily used for learning to represent and compress data, variations such as denoising autoencoders (e.g. in images) can be used for generative tasks.

An ai generative course involves feeding a model a large data set and optimizing its parameters to minimize the difference between the generated output and the real information. A model's ability to produce realistic and rich content depends on the complexity of its architecture, the quality and quantity of training data, and the optimization techniques used during training!