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Generative AI courses

1093 Courses

California Institute of Technology Northeastern University

Generative AI: Foundations and Concepts

This course provides an overview of some different concepts underpinning Generative AI, their mathematical principles, and their applications in engineering. The focus will be on the practical implementation of generative AI including, neural networks, attention mechanism, and advanced deep learning models.
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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

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 Coursera

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 Agents: Transform Your Organization

Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reason.
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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

Generative AI for Sustainability

Empower Change: Leverage Generative AI for Environmental and Social Impact
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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 Southern New Hampshire University

Generative AI Data Analyst

This Specialization equips you with the skills to become a Generative AI Data Analyst, using ChatGPT to streamline data analysis, automate repetitive tasks, and uncover insights faster. You’ll learn to work with spreadsheets, databases, and unstructured documents—transforming raw data into compelling stories and visualizations. By mastering these A.
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California Institute of Technology Coursera

Transforming Exploratory Data Analysis with AI

Picture this: You’re a data scientist working for a non-profit organization responding to a natural disaster. You’ve been tasked with analyzing data from multiple sources—satellite imagery, social media posts, and relief agency reports—to identify the most affected areas and allocate resources efficiently. The problem? The data is massive, unstruct.
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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!