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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 comprehensive course equips you with the skills to integrate Large Language Models (LLMs) and generative AI into your applications using Microsoft’s powerful Semantic Kernel. Whether you're aiming to create intelligent agents or build advanced chat.
provider Coursera
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California Institute of Technology FutureLearn

AI in Education

Join a rapidly evolving conversation on artificial intelligence's role in education. Offered by King’s College London, this online course delves into the opportunities, challenges, and ethical considerations that AI brings to learning environments globally. ‘AI in Education’ is a reimagined course derived from the previous ‘Generative AI in Higher.
provider FutureLearn
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California Institute of Technology Coursera

Generative AI for Change Management

Embark on a transformative journey with our course on Generative AI for Change Management, designed to equip you with the skills needed to lead successful AI adoption in your organization. This comprehensive program focuses on the human aspects of AI integration, teaching you how to manage change effectively, align AI initiatives with over.
provider Coursera
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California Institute of Technology Coursera

AWS AI Practitioner

Embark on your journey to build a solid foundation in Artificial Intelligence (AI) and Machine Learning (ML) with the AWS AI Practitioner Specialization. Designed for enthusiasts looking to leverage AWS cloud services, this specialization aligns with the AWS AI Practitioner Certification exam, ensuring a holistic understanding of AI concepts, g.
provider Coursera
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California Institute of Technology Pluralsight

Introduction to Developing AI Agents

AI agents are set to become a key force in the evolution of artificial intelligence, especially in the near future. These agents are transforming generative AI by harnessing the power of LLMs as reasoning engines and using tools to execute multistep tasks. In this comprehensive course, "Introduction to Developing AI Agents," you will learn t.
provider Pluralsight
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California Institute of Technology Coursera

GenAI for Learning and Development

Generative AI is setting new standards across industries, and Learning and Development is no exception. A McKinsey & Company report highlights AI's potential to reduce training task times by up to 40%, while MIT researchers reveal that AI can help professionals complete tasks 37% faster, improving output quality by 20%. Clearly, Generative AI.
provider Coursera
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California Institute of Technology Coursera

Generative AI in Marketing

Generative AI is reshaping the marketing field by automating creative tasks, enhancing personalization, and optimizing campaigns on a massive scale. This comprehensive course delves into the principles, models, and tools of Generative AI, focusing on elevating content creation, boosting customer engagement, and refining campaign strategies. P.
provider Coursera
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California Institute of Technology Coursera

Generative AI in Finance

Join our course, "Generative AI in Finance," for an in-depth understanding of how AI is revolutionizing the financial sector. Explore foundational concepts, key AI models, and their applications in financial analysis, decision-making, and compliance. Target Audience: Finance Professionals & Analysts: Gain insights and automation skills.
provider Coursera
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California Institute of Technology Coursera

Generative AI for Program Managers

Are you a program or project professional eager to harness the power of generative AI (GenAI)? The Generative AI for Program Managers specialization equips you with the skills to leverage AI-driven transformation. Enhance decision-making, optimize workflows, and improve stakeholder engagement in just 3 weeks. Explore the fundamentals of GenA.
provider Coursera
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California Institute of Technology Coursera

H2O.ai Agents : From Theory to Practice

Embark on an enlightening journey with the "H2O.ai Agents: From Theory to Practice" course. This comprehensive program offers a bridge between theoretical insights and practical AI agent implementation, guiding you through their complete lifecycle. Start by delving into core concepts, understanding AI agents' fundamental architecture, and how.
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!