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Generative AI courses

659 Courses

California Institute of Technology Coursera

Future Role Workshop Lab

Future Role Workshop Lab | Coursera Note: This lab is intended to be a live workshop, and not to be taken as a standalone course. Through this workshop, you will go hands-on with Generative AI to sharpen your current role description and anticipate how your role may change in the future. Anticipating how your role may change will hel.
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California Institute of Technology Coursera

GenAI for Customer Service Teams

GenAI for Customer Service Teams "GenAI for Customer Service Teams" is an introductory course designed to bridge the gap between generative AI (GenAI) technologies and customer service practices. This course demystifies GenAI, making it accessible for customer service professionals to enhance team coordination, automate tasks,.
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California Institute of Technology Coursera

GenAI for Application Developers

GenAI for Application Developers “GenAI for Application Developers” is designed for professionals eager to integrate AI into their development workflow. This comprehensive course introduces Gemini for Google Cloud (Duet AI), emphasizing its potential to streamline coding, debugging, and deployment processes. Participants will gain hands-on expe.
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California Institute of Technology Coursera

GenAI for Data Scientists

GenAI for Data Scientists GenAI for Data Scientists is tailored for professionals eager to integrate Generative AI (GenAI) into their data science practices. This introductory course simplifies the complex realm of GenAI, illustrating its remarkable impact on data analysis, predictive modeling, and more. You will gain a thorough understanding o.
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California Institute of Technology Coursera

GenAI for Software Engineering Teams

GenAI for Software Engineering Teams "GenAI for Software Engineering Teams" is an intensive one-hour course designed to transform the way software engineering teams operate. This course places a strong emphasis on collaborative methodologies and tools, empowering software engineering teams to enhance cross-functional integration, teamwork, and ov.
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California Institute of Technology Coursera

Gemini for Data Scientists and Analysts - Español

En este curso, descubrirás cómo Gemini, un colaborador potenciado por IA generativa de Google Cloud, ayuda a analizar los datos de los clientes y predecir las ventas de productos. También aprenderás a identificar, categorizar y desarrollar los clientes nuevos usando datos de clientes en BigQuery. A través de labs prácticos, comprobarás cómo Gem.
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California Institute of Technology Coursera

Gemini for end-to-end SDLC - Español

Gemini para SDLC de extremo a extremo - Español En este curso, descubrirás cómo Gemini, un colaborador potenciado por IA generativa de Google Cloud, te ayudará a usar los productos y servicios de Google para desarrollar, probar, implementar y administrar aplicaciones. Con la ayuda de Gemini, aprenderás a desarrollar y compilar una aplicación we.
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California Institute of Technology Coursera

Introduction to Large Language Models - Deutsch

Introduction to Large Language Models - Deutsch In diesem Einführungskurs im Microlearning-Format wird untersucht, was Large Language Models (LLM) sind, für welche Anwendungsfälle sie genutzt werden können und wie die LLM-Leistung durch Feinabstimmung von Prompts gesteigert werden kann. Darüber hinaus werden Tools von Google be.
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California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Japanese)

AWS ML Engineer Associate Curriculum のこの入門コースでは、機械学習 (ML) の基礎を復習し、ML と AI の進化について確認します。ML ライフサイクルの最初のステップとして、ビジネス目標を特定し、そのビジネス目標に基づいて ML の問題を定式化します。最後に、ML モデルの構築、トレーニング、デプロイに使用できるフルマネージド型 AWS サービスである Amazon SageMak.
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California Institute of Technology AWS Skill Builder

AWS ML Visão geral do curso de engenheiro associado (Português) | AWS ML Engineer Associate Curriculum Overview (Portuguese)

Neste curso introdutório à grade curricular de engenheiros de ML associados da AWS, você analisa os conceitos básicos de machine learning (ML) e examina a evolução do machine learning e da IA. Você explora as primeiras etapas do ciclo de vida do ML, identificando uma meta de negócios e formulando um problema de ML com base nessa meta de negócios. F.
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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!