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Generative AI courses

731 Courses

California Institute of Technology Coursera

Work with Gemini Models in BigQuery - 日本語版

Gemini モデルを使用した BigQuery 作業 - 日本語版 このコースでは、BigQueryで生成型AI作業にAI/MLモデルを使用する方法を紹介します。顧客関係管理に関連する実際の使用事例を通じて、Geminiモデルを使用してビジネス問題を解決するワークフローを解説します。理解を深めるために、SQLクエリとPythonノートブックを使用したコードソリューションを詳細に案内します。
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California Institute of Technology Coursera

Work with Gemini Models in BigQuery - 简体中文

Work with Gemini Models in BigQuery - 简体中文 이 과정은 BigQuery에서 생성형 AI 작업에 AI/ML 모델을 사용하는 방법을 보여줍니다. 고객 관계 관리와 관련된 실제 사용 사례를 통해 Gemini 모델로 비즈니스 문제를 해결하는 워크플로를 설명합니다. 이해를 돕기 위해 SQL 쿼리와 Python 노트북을 사용하는 코딩 솔루션을 단계별로 안내합니다. University: Provider:.
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California Institute of Technology Coursera

Gemini in Google Drive - Bahasa Indonesia

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California Institute of Technology Coursera

Gemini in Google Drive - Français

Gemini in Google Drive - Français Gemini pour Google Workspace est un module complémentaire qui permet aux utilisateurs d'accéder à des fonctionnalités d'IA générative. Ce cours explore les fonctionnalités de Gemini dans Google Drive au moyen de vidéos pédagogiques, d'activités pratiques et d'exemples concrets. À la fin de ce cours, vous dispose.
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California Institute of Technology Coursera

Gemini in Google Drive - 繁體中文

Gemini in Google Drive - 繁體中文 Gemini for Google Workspace 是一項外掛程式，可讓使用者存取生成式 AI 功能。本課程使用影片、實作活動和練習範例，深入介紹 Google 雲端硬盤中的 Gemini 的功能。 課程結束後，您將具備 Google 雲端硬盤中的 Gemini 的知識及技能，可自信地運用這項工具提升工作流程的效率。 提供者: Coursera 分類:.
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California Institute of Technology Coursera

Work with Gemini Models in BigQuery - Español

Trabaja con Modelos de Gemini en BigQuery - Español En este curso, se muestra cómo usar modelos de IA/AA para tareas de IA generativa en BigQuery. A través de un caso de uso práctico relacionado con la administración de relaciones con clientes, conocerás el flujo de trabajo para solucionar un problema empresarial con modelos de Gemini. Para fac.
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California Institute of Technology Coursera

Work with Gemini Models in BigQuery - 한국어

Work with Gemini Models in BigQuery - 한국어 이 과정은 BigQuery에서 생성형 AI 작업에 AI/ML 모델을 사용하는 방법을 보여줍니다. 고객 관계 관리와 관련된 실제 사용 사례를 통해 Gemini 모델로 비즈니스 문제를 해결하는 워크플로를 설명합니다. 이해를 돕기 위해 SQL 쿼리와 Python 노트북을 사용하는 코딩 솔루션을 단계별로 안내합니다. 제공자: Coursera 카테고.
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California Institute of Technology AWS Skill Builder

AWS Flash – AWS AI/ML Essentials (Simplified Chinese) (中文讲师定制版)

AWS Flash – AWS AI/ML Essentials (Simplified Chinese) (中文讲师定制版) 本课程面向需要参加 Amazon Web Services Certified AI Practitioner (AIF-C01) 认证考试的技术人员，通过此课程的学习了解认证考试的流程，注意事项，有助于学员加强对认证考试范围内的知识点的理解与掌握，同时借助于对样题的分析和讲解，更好地了解与熟悉认证考试的难度系数和考试形式。 级别：.
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California Institute of Technology Northeastern University

Ethics and Governance in the Age of Generative AI

Ethics and Governance in the Age of Generative AI This course is ideal for individuals looking to deepen their understanding of generative AI and best practices for ethical incorporation in workflows. Offered by Northeastern University on Coursera, it delves into the ethical and technical aspects of AI model development and deployment, with a co.
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California Institute of Technology AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Indonesian)

Fundamentals of Machine Learning and Artificial Intelligence (Indonesian) Dalam kursus ini, Anda akan belajar tentang dasar-dasar machine learning (ML) dan kecerdasan buatan (AI). Anda akan melihat berbagai bentuk hubungan antara AI, ML, deep learning, dan bidang kecerdasan buatan generatif yang sedang berkembang (AI generatif). Anda akan mendapat.
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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!