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Generative AI courses

659 Courses

California Institute of Technology University of Michigan

Using Generative AI for Learning Design Activities

Using Generative AI for Learning Design Activities | University of Michigan | Coursera In “Using Generative AI for Learning Design Activities,” you’ll experiment with how generative AI supports learning design tasks. Throughout the course, instructors will use an applied case study to illustrate how generative AI can enhance learning desi.
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California Institute of Technology Coursera

Generative AI in Marketing

Generative AI in Marketing Discover the transformative potential of Generative AI in the marketing landscape. This course offers an introduction to generative AI and explores its unique capabilities in the context of marketing. Learn to create compelling visual content and enhance customer engagement through advanced AI tools. Dive into the int.
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California Institute of Technology Indian Institute of Technology Guwahati

Generative AI for Healthcare Students and Professionals

Generative AI for Healthcare Students and Professionals This course aims to provide healthcare students and professionals with a solid foundation in how generative AI is used in their sector, adopting a balanced discourse of information. Participants will engage with case studies that analyze the current landscape of AI in various fields such as.
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California Institute of Technology Coursera

Capstone: Making the Case for AI

Capstone: Making the Case for AI Capstone: Making the Case for AI Put everything you learned in the AI in Marketing Specialization into practice in this capstone project. This course invites learners to delve into AI implementations in marketing—connecting to customer equity, showcasing generative AI's content production ca.
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California Institute of Technology Coursera

Driving Customer Equity with AI

Title: Driving Customer Equity with AI Description: This course covers how to drive customer equity and transform your marketing strategies using generative AI. Delve into the foundational components of customer equity and understand the impacts on long-term business success. Learn to evaluate marketing tactics through the lens of customer equit.
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California Institute of Technology University of Michigan

Generative AI as a Learning Design Partner

Generative AI as a Learning Design Partner In "Generative AI as a Learning Design Partner," you will explore opportunities and applications for generative AI in supporting and enhancing learning design activities and tasks. Discover how generative AI tools, like ChatGPT, can be utilized to create learning outcomes, draft course outlines, conte.
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California Institute of Technology edX

HP AI Teacher Academy

HP AI Teacher Academy Please stay on the AUDIT path for free access. Digital Promise and HP will provide a letter of completion with course hours upon completing the 5 modules for this course when using the free audit path. Additionally, you will not have a time limit to complete this course. There is no difference in content between the audit (fr.
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California Institute of Technology Coursera

Gemini in Google Drive

Gemini for Google Workspace is an innovative add-on that grants users access to cutting-edge generative AI features. This comprehensive course explores the full potential of Gemini in Google Drive through engaging video lessons, hands-on activities, and practical examples. By the end of this course, you'll gain the expertise to confidently harne.
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California Institute of Technology Coursera

Generative AI for Business Consultants

Generative AI for Business Consultants This specialization helps learners leverage Gen AI tools effectively in a consulting framework. With a focus on responsible and ethical use, gain a balanced view on utilizing Gen AI in practical work situations, addressing complex challenges, and delivering significant value to.
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California Institute of Technology MasterClass

Achieve More With Gen AI

Achieve More With Gen AI | MasterClass Achieve More With Gen AI Don’t fall behind in the AI era. Learn how to boost your skills, enhance your creativity, and navigate ethical AI use. Supercharge your productivity, master the prompt, and automate routine tasks with some of the most followed voices in AI today: Ethan Mollick,.
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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!