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Generative AI courses

1062 Courses

California Institute of Technology Udemy

GenAI For Finance & Accounting : Complete Masterclass 2025

Join the GenAI For Finance & Accounting: Complete Masterclass 2025 to revolutionize your accounting and finance skills. This comprehensive course focuses on integrating Generative AI technologies to enhance productivity and streamline your workflows. Offered by Udemy, this masterclass covers a wide array of topics, including: Generative AI in.
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California Institute of Technology Udemy

GenAI Governance

Immerse yourself in the world of Generative AI Governance with this comprehensive course aimed at equipping you with essential skills and knowledge. Dive deep into the frameworks that shape the landscape of responsible AI implementation, understand risk management techniques, and navigate the complexities of compliance. Offered by Udemy, thi.
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California Institute of Technology Coursera

(KK) Generative AI with Large Language Models

Coursera Dive into the fundamentals of generative AI with our comprehensive course on Large Language Models (LLMs). This program is designed to equip learners with essential knowledge to deploy generative AI in practical scenarios. Understand the lifecycle of LLM-based generative AI and the key steps involved, including data gathering, model.
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California Institute of Technology Coursera

GenAI for Compensation, Benefits and Payroll

In an era of digital transformation in HR, the course "GenAI for Compensation, Benefits, and Payroll" offers a thorough guide to leveraging AI for improving these key functions. Tailored for both novices and intermediate professionals, this course emphasizes a hands-on approach to integrating AI into payroll automation and benefits personaliz.
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California Institute of Technology Coursera

GenAI for Clinical Documentation

Join the "GenAI for Clinical Documentation" course and explore how Generative AI can revolutionize your healthcare documentation process. This course is designed for healthcare professionals, administrators, and IT staff, offering valuable insights into incorporating AI to optimize patient notes and reduce documentation errors. Begin by und.
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California Institute of Technology ESSEC Business School

Introduction to Artificial Intelligence

Introduction to Artificial Intelligence Course - Learn AI Fundamentals Artificial Intelligence is transforming industries and redefining business strategies. Are you prepared to harness its potential? Enroll in this captivating online course to explore AI's fundamentals, tracing its historical roots and cutting-edge innovations in machine learni.
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California Institute of Technology CodeSignal

Generative AI in 2025 - Overview and Practice

Join us for an in-depth exploration of the transformative power of generative AI in 2025. This course offers a thorough understanding of how generative AI works and its real-world applications, while also delving into future trends and potential limitations. Gain essential insights that will help you expertly navigate an AI-driven future. Wheth.
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California Institute of Technology CodeSignal

Applying Generative AI in Everyday Professional Tasks

Discover the potential of generative AI to transform your professional tasks. This comprehensive course offers insightful ways to apply AI in content creation, writing, information summarization, and routine activity management. Learn to utilize AI tools to enhance efficiency and improve productivity in your day-to-day work. Join us at CodeSig.
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California Institute of Technology CodeSignal

Generative AI - The Next Frontier: Voice, Video, and More

Generative AI - The Next Frontier: Voice, Video, and More Artificial Intelligence Courses Computer Vision Courses Deep Learning Courses Neural Networks Courses Generative AI Courses Speech Synthesis Courses Text to Speech Courses Multimodal AI Courses Discover the exciting possibilities of generative AI as it moves beyond the r.
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California Institute of Technology CodeSignal

Generative AI for Everyone in 2025

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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!