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Generative AI courses

1093 Courses

California Institute of Technology Udemy

Art with AI Bootcamp: From Pixel Dummy to Legend Artist

Transform Your Design Skills with AI: Craft Stunning Visuals from Figma Wireframes to DALL·E Masterpieces
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California Institute of Technology Udemy

CEO Playbook: Generative AI

Master Generative AI: A CEO’s Blueprint for Transforming Business
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California Institute of Technology Udemy

Generative AI for Sustainability

Empower Change: Leverage Generative AI for Environmental and Social Impact
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California Institute of Technology Udemy

Prompt Perfect

AI writing skills for the modern professional
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California Institute of Technology Udemy

Master Generative AI for Software Testing: Manual to Auto

Master Generative AI for Testing: Python, Playwright, and Behave BDD Frameworks for Manual and Automation Testers
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California Institute of Technology Udemy

Governance for Generative AI

Learn Governance for Generative AI | Understand how to build a GenAI Governance Program | Elevate your Career
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California Institute of Technology Coursera

Generative AI for Educators

Over half of higher education institutes have already investigated using generative AI (gen AI) in their teaching practices to create engaging learning experiences (Gartner)! This Generative AI for Educators specialization teaches educators and administrators how to leverage gen AI effectively to reinvent engaging and future-ready learning solution.
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California Institute of Technology Udemy

Generative AI for upskilling and learning initiatives

Be an expert at Generative AI in learning & upskilling & Learn how to use Generative AI in learning and development
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California Institute of Technology Coursera

H2O Generative AI Starter Track

H2O Generative AI Starter Track introduces you to the practical applications of Generative AI technology using h2oGPTe, H2O's advanced enterprise platform. This beginner friendly course provides a structured pathway from foundational concepts to hands-on implementation. You will begin with core Generative AI principles before essential developing p.
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California Institute of Technology Udemy

Complete Generative AI for upskilling & learning initiatives

Master Generative AI in Personalized Learning and Upskilling and Learn Various types of Generative AI Models!
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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!