Join

Generative AI courses

1063 Courses

California Institute of Technology University of Michigan

GenAI in Business: Discover the Possibilities

Embark on the first course in the Generative AI in Business series, where you'll be introduced to the "See" phase of the See, Plan, Act framework. This course offers an in-depth exploration of how generative AI is revolutionizing industries by automating routine tasks and enhancing customer interactions and strategic insights. Through an ins.
course image
California Institute of Technology University of Michigan

GenAI in Business: Planning Framework for Implementation

GenAI in Business: Planning Framework for Implementation - University of Michigan | Coursera Join the University of Michigan's 'GenAI in Business: Planning Framework for Implementation' course on Coursera, an insightful journey into the planning phase of AI implementation in business. This course delves into the "Plan" stage of the "See, Plan,.
course image
California Institute of Technology Coursera

Advanced Data Analysis with Generative AI

Join the course 'Advanced Data Analysis with Generative AI' to explore sophisticated analytical methods using AI. You'll gain hands-on experience with predictive modeling, time-series forecasting, and anomaly detection to uncover patterns in complex datasets. This course also covers text data analysis, which allows you to derive insights from un.
course image
California Institute of Technology Coursera

Scenario and Root Cause Analysis with Generative AI

Join our comprehensive course on Scenario and Root Cause Analysis with Generative AI offered by Coursera. This program is designed to equip you with the skills to apply generative AI in scenario planning and root cause analysis. Throughout the course, you will: Conduct scenario analysis using advanced generative AI models Perform root c.
course image
California Institute of Technology Coursera

Coding and Automation for Data Analysis with Generative AI

Embark on a transformative journey with our course on Coding and Automation for Data Analysis with Generative AI. Designed for aspiring data analysts and seasoned professionals alike, this course will guide you through using AI-powered tools for optimized code generation. Focus on mastering SQL, Python, and R for efficient data analysis as you.
course image
California Institute of Technology Coursera

Introduction to Generative AI for Data Analysis

Welcome to the Introduction to Generative AI for Data Analysis, a foundational course designed to provide you with a comprehensive understanding of generative AI and its practical applications in the field of data analysis. Throughout this course, you will: Define generative AI and understand its pivotal role in data analysis. Explor.
course image
California Institute of Technology SAP Learning

AI Ethics at SAP

SAP recognizes the transformative potential of artificial intelligence (AI) for businesses, governments, and society. However, the rapid adoption of AI technologies brings with it various economic, political, and social challenges. The current pace of technological advancement has outstripped the establishment of sufficient governmental guidelin.
California Institute of Technology AWS Skill Builder

AWS Flash - Chalk Talks: Generative AI on AWS (Simplified Chinese)

本课程概述了可供客户在 AWS 上构建生成式人工智能应用程序的选项。 本课程将着重介绍如何利用 Amazon SageMaker Jumpstart 上的基础模型来构建生成式人工智能应用程序。 课程级别：基础级 时长：55 分钟 注意：本课程具有本地化的注释/字幕。 旁白保留英语。要显示字幕，请单击播放器右下角的 CC 按钮。 课程内容 本课程包括幻灯片内容和演示。 课程目标 在本课程中.
course image
California Institute of Technology AWS Skill Builder

AWS Flash - Generative AI in Action: Real-World Use Cases (Japanese)

このコースでは、生成 AI のユースケースとそのビジネス価値の概要を提供します。主要産業における実際の応用方法とケーススタディを含んでいます。コースレベルは基礎で、所要時間は75分です。 アクティビティ: このコースには、プレゼンテーション、実際の例、ケーススタディが含まれます。 コースの目標: 生成 AI の4つのコア目標を理解する 会話型 AI とパーソナラ.
course image
California Institute of Technology AWS Skill Builder

AWS Flash - Generative AI in Action: Real-World Use Cases (Traditional Chinese)

本課程概述了生成式 AI 的使用案例，及其帶來的商業價值。內容涵蓋生成式 AI 在各大產業中的實際應用和案例研究。 課程等級：基礎 持續時間：75 分鐘 注意：本課程具有本地化的註釋/字幕。旁白保留英語。要顯示字幕，請按一下播放器右下角的 CC 按鈕。 本課程內容包括簡報、實際範例和案例研究。 課程目標中，您將學習以下內容： 了解生成式 AI 的四個核心目標.
course image

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!