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Generative AI courses

1018 Courses

California Institute of Technology Udemy

Practical GenAI: Basics, Tools, Use cases, Ethics, Future

Welcome to Practical GenAI, a comprehensive guide perfect for new business employees looking to excel in the evolving world of AI technologies. This Udemy course covers the basics, tools, use cases, ethical considerations, and future prospects of AI in various business domains. Key Learning Areas: Automation: Discover how AI can streamline an.
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California Institute of Technology Udemy

Prompt Engineering PRO (ChatGPT & Generative AI FAST-TRACK)

Elevate your skills with our Prompt Engineering PRO course, designed for fast-tracking expertise in ChatGPT and Generative AI. Learn effective tactics, enhance your content creation, and improve customer support strategies. As a bonus, this course includes a free AI Prompt Tool to maximize your learning potential. Perfect for those looking to.
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California Institute of Technology Udemy

Hands-on SAP Build Code,JOULE AI, Low Code No Code,CAPM,HANA

Join our comprehensive course on Udemy to delve into SAP Build Code, exploring the Cloud Application Programming Model and the exciting realm of Generative Artificial Intelligence. This hands-on course is tailored for individuals eager to develop expertise in Low Code No Code methodologies, JOULE, and HANA integration. Perfect for tech enthusi.
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California Institute of Technology Udemy

Adobe Firefly Mastery Course - Crafting Magic with Firefly

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California Institute of Technology edX

Generative AI for Everyone

97% of employers anticipate the benefits of generative AI (Source: Access Partnership survey for Amazon Web Services). This Generative AI Professional Certificate equips you with the skills to automate tasks, accelerate research, swiftly analyze data, and craft original content. Whether enhancing your resume or advancing your career, this progra.
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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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California Institute of Technology edX

Generative AI for Data Scientists

Are you eager to elevate your data science skills with generative AI? Tailored for data scientists, analysts, architects, engineers, and data enthusiasts, this program equips you with the essential knowledge to integrate gen AI into your work efficiently. Start by building a strong foundation in generative AI's real-world applications. Underst.
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California Institute of Technology edX

Generative AI for Software Developers

Generative AI (or Gen AI) is ushering in a new era of software development. By harnessing this cutting-edge technology, developers can write high-quality code faster, reduce bugs, and significantly enhance their efficiency. As AI becomes an essential tool for modern software engineering, mastering gen AI is no longer optional, it’s a must-have.
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California Institute of Technology edX

Generative AI for Data Analysts

Data analytics plays a crucial role in helping organizations make informed decisions in today's data-driven landscape. As demand for proficient professionals in this field rises, it is essential to stay current with cutting-edge advancements. Generative AI represents one of the most exciting evolutions in this realm, providing powerful resources.
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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!