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Generative AI courses

541 Courses

California Institute of Technology Coursera

Smarter Content Creation with Microsoft Copilot

Smarter Content Creation with Microsoft Copilot This course is the third in a series designed to revolutionize your marketing capabilities with Microsoft Copilot's GenAI. Focusing on content creation, you’ll explore the diverse types of content you can create using Microsoft Copilot. Next, you’ll learn how to brainstorm content ideas and effecti.
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California Institute of Technology University of Naples Federico II

Developing Explainable AI (XAI)

Developing Explainable AI (XAI) As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course pro.
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California Institute of Technology Coursera

GenAI for Sales Operations Specialists

GenAI for Sales Operations Specialists This course introduces Sales Operations Specialists to the transformative capabilities of Generative Artificial Intelligence (GenAI). Participants will explore practical strategies to leverage GenAI in various sales operations tasks, enhancing efficiency, productivity, and strategic insights. Through a com.
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California Institute of Technology Coursera

Gemini in Google Drive - 한국어

Gemini in Google Drive - 한국어 University: 제공: Coursera Categories: Generative AI Courses Google Workspace Courses Gemini Courses Google Workspace를 위한 Gemini는 사용자에게 생성형 AI 기능에 대한 액세스를 제공하는 부가기능입니다. 이 과정은 동영상 강의, 실습, 실제 사례를 사용하여 Google Drive의 Gemini가 제공하는 기능.
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California Institute of Technology Coursera

Gemini in Google Drive - 日本語版

Gemini in Google Drive - 日本語版 Gemini for Google Workspace は、生成 AI 機能へのアクセスをユーザーに提供するアドオンです。動画レッスン、ハンズオンアクティビティ、実用的な例を使用して、Gemini in Google ドライブの機能について詳しく説明します。このコースを修了すると、自信を持って Gemini in Google ドライブを活用し、ワークフローを改善する.
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California Institute of Technology Coursera

GenerativeAI for Customer Success

GenerativeAI for Customer Success Did you know that companies using Generative AI in customer success see a significant improvement in customer satisfaction and operational efficiency? Discover how you can leverage this technology to transform your customer success strategies. This short course empowers customer success professionals and manager.
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California Institute of Technology Coursera

Project: Generative AI Applications with RAG and LangChain

Project: Generative AI Applications with RAG and LangChain Get ready to put all your generative AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world generative AI application. During this course.
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California Institute of Technology Coursera

Introduction to Generative AI for Developers With Copilot

Introduction to Generative AI for Developers With Copilot This course introduces developers to generative AI technologies, focusing on their practical applications in software development. You will explore the core concepts of generative AI and understand the basic functionalities and ethical considerations of gener.
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California Institute of Technology Coursera

Data Preparation and Evaluation with Copilot

Data Preparation and Evaluation with Copilot - Coursera This course prepares you to use Microsoft Copilot for data preparation and evaluation tasks. You'll learn how to leverage Copilot's generative AI and natural language processing capabilities to streamline your workflow, ensure data quality, and generate valuabl.
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California Institute of Technology Coursera

Introduction to Microsoft Copilot

Introduction to Microsoft Copilot In this introductory course, you'll embark on a journey into the world of generative AI and Microsoft Copilot. We'll demystify the concepts behind this transformative technology, exploring its potential and limitations. You'll gain a clear understanding of what generative AI is, how it works,.
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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!