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Generative AI courses

542 Courses

California Institute of Technology Coursera

Integrating Applications with Gemini 1.0 Pro on Google Cloud

This short course on integrating applications with Gemini 1.0 Pro models on Google Cloud helps you discover the Gemini API and its generative AI models. The course teaches you how to access the Gemini 1.0 Pro and Gemini 1.0 Pro Vision models from code. It lets you test the capabilities of the models with text, image, and video prompts from an a.
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California Institute of Technology edX

Using GenAI to Automate Software Development Tasks

Using GenAI to Automate Software Development Tasks In this cutting-edge course, you will: Learn how to leverage generative AI to streamline your development workflow Explore AI pair programming tools like CodeWhisperer to boost productivity Master prompt engineering techniques to guide AI models and shape outputs Understand the role o.
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California Institute of Technology Coursera

Fundamentals of Generative AI for Beginners

Fundamentals of Generative AI for Beginners Generative AI is a disruptive technology with immense impact. It is pushing businesses to rethink customer strategies, encouraging chipmakers to meet increased processor demands, and leading academia to alter learning paths and curriculums across various fields of study. This course introduces you to.
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California Institute of Technology edX

Developing Generative AI Applications with Python

Developing Generative AI Applications with Python Generative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stan.
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California Institute of Technology Coursera

Generative AI for Data Science

Generative AI for Data Science Discover how Generative AI can enhance data accuracy and operational efficiency in data science. This short course is designed for data scientists and AI enthusiasts to unlock the potential of Generative AI in data-driven projects. In just 3 hours, you'll learn to: Explore and leverage Generative AI application.
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California Institute of Technology Coursera

Generative AI: Unleash Your Project Management Potential

Generative AI: Unleash Your Project Management Potential By 2030, 80 percent of project management work will be done by AI (Gartner), enabling project managers to boost efficiency by automating tasks, generating insights from vast data sets, and speeding up processes. This course gives existing and aspiring project managers the job-ready skills in.
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California Institute of Technology Coursera

Ethical and Regulatory Implications of Generative AI

Ethical and Regulatory Implications of Generative AI Did you know that the global AI market size is projected to reach $997.77 billion by 2028? As AI technologies rapidly integrate into every sector, understanding their ethical and regulatory implications has never been more critical. This Short Course was created to help professionals in tech, la.
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California Institute of Technology Coursera

Generative AI - Your Personal Code Reviewer

Generative AI - Your Personal Code Reviewer Generative AI - Your Personal Code Reviewer Dive into the forefront of coding evolution with our comprehensive course, "Generative AI - Your Personal Code Reviewer." This immersive course is tailored for learners seeking to unlock the full potential of generative AI in the world of code review and.
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California Institute of Technology Coursera

Generative AI Essentials: A Comprehensive Introduction

Generative AI Essentials: A Comprehensive Introduction Welcome to this introductory course on Generative AI, designed to empower you with the knowledge to harness this cutting-edge technology. This course offers a deep dive into the core concepts of Generative AI, exploring its ability to create new content from existing data a.
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California Institute of Technology AWS Skill Builder

Foundations of Prompt Engineering (Indonesian)

Foundations of Prompt Engineering (Indonesian) Dalam kursus ini, Anda akan mempelajari prinsip, teknik, dan praktik terbaik untuk mendesain prompt yang efektif. Kursus ini memperkenalkan dasar rekayasa prompt dan berlanjut ke teknik prompt lanjutan. Anda juga akan mempelajari cara mencegah penyalahgunaan prompt dan cara mengurangi bias saat berint.
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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!