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Generative AI courses

1093 Courses

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California Institute of Technology Coursera

Semantic Kernel SDK for Intelligent Applications

Unlock the full potential of AI-driven business solutions with the Semantic Kernel SDK. This course equips you with the knowledge to integrate Large Language Models (LLMs) and generative AI into your applications using Microsoft’s Semantic Kernel. You'll gain a deep understanding of how to create intelligent agents, build chat applications, and des.
provider Coursera
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California Institute of Technology FutureLearn

AI in Education

Navigate and understand the complex impacts of AI on education Artificial intelligence is reshaping education at an extraordinary pace. Educators and learners around the globe are navigating this new landscape with curiosity, excitement, and caution. This online course, designed by King’s College London, invites you to explore the opportunities, c.
provider FutureLearn
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California Institute of Technology Coursera

Generative AI for Change Management

In this course, you’ll learn how to lead your organization through the transformative shift of adopting and integrating Generative AI. You’ll focus on the human side of AI adoption—managing change, aligning AI initiatives with business goals, engaging stakeholders, and building a culture of innovation. Through expert-led videos, hands-on labs, real.
provider Coursera
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California Institute of Technology Coursera

AWS AI Practitioner

The AWS Certified AI Practitioner Specialization is meant for those who desire to establish a solid base in Artificial Intelligence (AI) and Machine Learning (ML) and have the ability to utilize AWS cloud services. This specialization is aligned with the AWS AI Practitioner Certification exam and offers a broad understanding of AI concepts, generat.
provider Coursera
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California Institute of Technology Pluralsight

Introduction to Developing AI Agents

AI agents are set to become a key driver in the evolution of artificial intelligence in the near term. AI agents are revolutionizing generative AI by leveraging LLMs as reasoning engines and using tools (function calls) to perform multistep tasks. In this course, Introduction to Developing AI Agents, you’ll learn to create these AI agents. First, y.
provider Pluralsight
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California Institute of Technology Coursera

GenAI for Learning and Development

Generative AI is revolutionizing industries across the board – and L&D is no exception. According to a recent McKinsey & Company report, AI could cut training task time by up to 40%. MIT researchers also found that AI tools can help workers, including learning and development professionals' complete tasks 37% faster and improve output quality by 20.
provider Coursera
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California Institute of Technology Coursera

Generative AI in Marketing

Course Description: Generative AI is revolutionizing the marketing landscape by automating creative processes, enhancing personalization, and optimizing campaigns at scale. This course provides a deep dive into Generative AI and its applications in marketing, focusing on the core principles, models, and tools that enhance content creation, customer.
provider Coursera
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California Institute of Technology Coursera

Generative AI in Finance

Course Description: This course provides a comprehensive exploration of Generative AI and its transformative impact on the financial sector. Learners will delve into foundational concepts, key AI models, and their applications across financial analysis, decision-making, forecasting, and compliance. With a focus on real-world case studies and hands-.
provider Coursera
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California Institute of Technology Coursera

Generative AI for Program Managers

Are you a program or project professional keen to harness generative AI (GenAI) to enhance decision-making, optimize workflows, and improve stakeholder engagement? This Generative AI for Program Managers specialization gives you the expertise to leverage AI-powered transformation to improve operational efficiency, mitigate risk, and enhance strateg.
provider Coursera
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California Institute of Technology Coursera

AWS Certified AI Practitioner

Welcome to the transformative journey that is the AWS Certified AI Practitioner Course! In today's rapidly changing AI landscape, having a firm grasp of AI concepts is critical, but knowing how to implement these concepts on AWS is where the challenge—and opportunity—lies. If you've ever felt overwhelmed by the complexities of integrating AI into A.
provider Coursera

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!