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Generative AI courses

1012 Courses

California Institute of Technology AWS Skill Builder

AWS Security: Securing Generative AI on AWS

This course was developed by members of AWS Technical Field Communities (TFC), an AWS community of technical experts. The content is intended to complement our standard training curriculum and augment your AWS learning journey.This course is for Security and AI/ML technical users who need to know about securing generative AI on AWS. You will learn.
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California Institute of Technology edX

Generative AI Fundamentals

Become proficient in Generative AI with this program that covers all the fundamentals. This program will allow you to get practical skills with major cloud providers like Azure and AWS as well as foundational knowledge on the core concepts you must know. Developed by industry experts, this program provides you with practical experience using vario.
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California Institute of Technology edX

Generative AI for Everyone

97% of employers expect to benefit from using generative AI (Source: Access Partnership survey for Amazon Web Services). Organizations need individuals who know how to automate tasks, speed up research, analyze data swiftly, and create original content using gen AI. This generative AI Professional Certificate is ideal for anyone looking to build i.
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California Institute of Technology edX

Generative AI for Cybersecurity

As cyber threats evolve, the tools needed to combat them are also evolving. Generative AI is rapidly becoming a vital asset in a cybersecurity professional’s toolkit. It is helping streamline threat detection, automate security operations, and enhance response strategies. With cybersecurity jobs projected to grow 32% by 2032 (U.S. Bureau of Labor.
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California Institute of Technology Coursera

GenAI for Underwriters: Enhancing Application Reviews

Welcome to this transformative course designed to help you revolutionize your underwriting process through the power of Generative AI (GenAI). In this course, you’ll learn how to streamline application reviews, enhance decision-making accuracy, and automate repetitive tasks to save valuable time. With no technical experience required, you’ll be abl.
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California Institute of Technology Georgia Institute of Technology

ChatGPT for Educators

In this program, educators of all levels will learn how to adjust to a world in which conversational AI has become as common as search engines and smartphones. This emerging technology demands some adjustments on the part of educators to make sure our homeworks and tests remain valid assessments of student learning, but adjusting to its existence.
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California Institute of Technology Southern New Hampshire University

AI Agents and Agentic AI in Python: Powered by Generative AI

AI agents are transforming the way we interact with technology, and in this specialization, you’ll learn how to build them from the ground up—without the need for complex or rigid frameworks that will be irrelevant next week. Whether you’re a beginner or an experienced developer, you’ll master the core concepts that power AI agents, giving you full.
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California Institute of Technology Southern New Hampshire University

Generative AI and ChatGPT for K-12 Educators

Despite what you may have heard, ChatGPT and other generative AI tools offer exciting possibilities for innovative teaching and personalized education. This hands-on specialization is designed for K-12 educators looking to harness AI to enhance lesson planning, engage students, and foster creativity—no prior experience with ChatGPT, AI, or prompt e.
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California Institute of Technology Northeastern University

AI Innovation in Healthcare

AI is transforming the landscape of healthcare by offering innovative solutions to improve diagnostics, personalize treatment plans, and streamline clinical workflows. This course from Northeastern University, available through Coursera, delves into the crucial role of AI in modern healthcare. Participants will learn how various data forms, f.
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California Institute of Technology edX

Generative AI for Data Engineering

Gartner forecasts suggest that by 2027, 80% of the engineering workforce will need to adapt to the rise of generative AI. For data engineers, AI proficiency is set to become crucial for career progression. Data Engineering involves the proficient collection, transformation, and storage of data. The application of generative AI tools enhances these.
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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!