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Generative AI courses

542 Courses

California Institute of Technology edX

AI Applications and Prompt Engineering

Explore the world of Artificial Intelligence with our Introduction to AI Applications and Prompt Engineering course. This engaging course is designed for individuals eager to broaden their understanding of AI applications and learn how to construct their own AI-powered projects. Throughout this course, you'll gain invaluable insights into: The ess.
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provider edX
pricing Free Online Course (Audit)
duration 1 week, 5-10 hours a week
sessions On-Demand
California Institute of Technology Coursera

Generative AI with Large Language Models

Embark on a transformative learning journey with the Generative AI with Large Language Models (LLMs) course. This intermediate-level course, available on Coursera and developed in collaboration with leading universities, dives deep into the world of generative artificial intelligence, unraveling the complexities of LLMs and their applications in re.
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provider Coursera
pricing Free Online Course (Audit)
duration 17 hours
sessions On-Demand
California Institute of Technology Udemy

Generative AI, from GANs to CLIP, with Python and Pytorch

Join our comprehensive course on Udemy titled "Generative AI, from GANs to CLIP, with Python and Pytorch." This course is designed to take you on a journey through the fascinating world of generative AI networks. Whether you are starting out or looking to deepen your understanding, this course covers everything from the basics to more advanced conc.
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provider Udemy
pricing Paid Course
duration 10 hours 34 minutes
sessions On-Demand
California Institute of Technology Coursera

Generative AI: Foundation Models and Platforms

Explore the transformative world of generative AI with our comprehensive course on "Generative AI: Foundation Models and Platforms", brought to you in collaboration with Coursera. Designed for both enthusiasts and seasoned practitioners, this course delves into the core concepts, models, and platforms that define the fast-evolving landscape of gene.
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provider Coursera
pricing Free Online Course (Audit)
duration 6-7 hours
sessions On-Demand
California Institute of Technology Coursera

Responsible AI in the Generative AI Era

Embark on a journey through the realms of Generative AI with our one-week microlearning course, "Responsible AI in the Generative AI Era", offered exclusively on Coursera. Delve into an insightful introduction to the principles of Responsible AI and discover their significance in the rapidly evolving Generative AI landscape. This comprehensive cour.
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provider Coursera
pricing Free Online Course (Audit)
duration 1-2 hours
sessions On-Demand
California Institute of Technology Coursera

Generative AI for Everyone

Generative AI for Everyone - a transformative course offered on Coursera and instructed by the renowned AI pioneer, Andrew Ng. Dive deep into the realm of generative AI with this comprehensive course designed to empower your work and innovation potential. Andrew Ng will provide an unparalleled perspective on generative AI, covering its mechanisms,.
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provider Coursera
pricing Free Online Course (Audit)
duration 6-7 hours
sessions On-Demand
California Institute of Technology Coursera

GenAI for Everyone

Unlock the potential of Generative AI with our "GenAI for Everyone" course available on Coursera. Dive deep into the essentials of GenAI, understanding its numerous capabilities and how it's transforming everyday life through applications like chatbots, virtual assistants, and personalized recommendation systems. This course is perfect for those ea.
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provider Coursera
pricing Free Online Course (Audit)
duration 9-10 hours
sessions On-Demand
California Institute of Technology FutureLearn

Microsoft Future Ready: Designing and Implementing an Azure AI Solution

Join the "Microsoft Future Ready: Designing and Implementing an Azure AI Solution" course, an immersive program aimed at enhancing your data science process through the power of Microsoft Azure. This course, curated by the Microsoft World Wide Learning team, is tailored for data scientists preparing for the DP-100: Designing and Implementing a Data.
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provider FutureLearn
pricing Free Online Course (Audit)
duration 3 weeks, 6 hours a week
sessions On-Demand
California Institute of Technology Coursera  Specialization

Getting Started with Generative AI API

Title: Getting Started with Generative AI API Description: Delve into the world of generative AI with a comprehensive specialization that explores its capabilities through Python. This program covers essential domains like text, image, and code generation with a focus on leveraging OpenAI's cutting-edge API. Participants will gain insights into the.
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provider Coursera  Specialization
pricing Paid Course
duration 13 weeks, 3 hours a week
California Institute of Technology Google Cloud Skills Boost

Introduction to Generative AI Studio

Join the Introduction to Generative AI Studio course brought to you by Google Cloud Skills Boost, designed for learners eager to master the intricacies of Generative AI. This course unpacks the fundamentals of Generative AI Studio, a premier tool within Vertex AI, crafted to empower users to prototype and tailor generative AI models for integration.
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provider Google Cloud Skills Boost
pricing Free Certificate
duration 8 hours
sessions On-Demand

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!