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Generative AI courses

542 Courses

California Institute of Technology Coursera

Generative AI for Data Science with Copilot

Generative AI for Data Science with Copilot This course provides a comprehensive introduction to Generative AI in data science. You'll explore the foundational concepts of generative AI, including GANs, VAEs, and Transformers, and discover how Microsoft Copilot leverages these models to streamline data science workflows. You'll gain hands-on exp.
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California Institute of Technology Coursera

GenAI for Computer Support Specialists (IT Support)

This course provides an opportunity to understand and utilize Generative AI (GenAI) in problem-solving and customer interactions. Participants will learn how to automate tasks, resolve issues more quickly with AI assistance, and improve customer support with personalized service. The program covers the core functionalities and limitations of.
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California Institute of Technology Coursera

GenAI for IT Project Managers

GenAI for IT Project Managers GenAI for IT Project Managers is designed to introduce IT project managers to the revolutionary world of GenAI technologies. This course provides an essential understanding of how GenAI can be integrated into IT project management to enhance efficiency, decision-making, and innovation. The content.
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California Institute of Technology Coursera

AI Engineering

AI Engineering This specialization teaches developers to build next-generation apps powered by generative AI. It covers topics like the OpenAI API, open-source models, AI safety, embeddings, vector databases, AI agents, how to speed up your AI development with LangChain, and much more. University: Coursera
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California Institute of Technology Coursera

Microsoft Copilot for Data Science

Microsoft Copilot for Data Science According to a 2023 Gartner study, 67% of mature organizations are creating new roles for GenAI, and 87% of these organizations have a dedicated AI team. Copilot for Data Science is tailored to help existing and aspiring data scientists prepare for these roles through a robust specialization designed to integrate.
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California Institute of Technology Coursera

Generative AI Engineering with LLMs

Generative AI Engineering with LLMs The Generative AI (Gen AI) market is anticipated to grow by 46% yearly until 2030 (Source: Statista). As a result, Gen AI engineers are in high demand. This program equips aspiring data scientists, machine learning engineers, and AI developers with vital skills in Gen AI, large language models (LLMs), and natu.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Thai)

Exploring Artificial Intelligence Use Cases and Applications (Thai) ในหลักสูตรนี้ คุณจะได้ศึกษากรณีใช้งานในสถานการณ์จริงว่าด้วยการนำปัญญาประดิษฐ์ (AI), แมชชีนเลิร์นนิง (ML) และปัญญาประดิษฐ์ช่วยสร้าง (AI ช่วยสร้าง) ไปใช้ในอุตสาหกรรมต่างๆ อาทิ อุตสาหกรรมการดูแลสุขภาพ การเงิน การตลาด ความบันเทิง และอื่นๆ อีกมากมาย นอกจากนี้คุณยังจะได้เรียนรู้เกี่ยวกั.
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California Institute of Technology Coursera

GenAI for Digital Marketing and Ecommerce Analysts

GenAI for Digital Marketing and Ecommerce Analysts In today's fast-paced e-commerce world, staying one step ahead is key to success. The "Gen AI for Digital Marketing and E-commerce Analyst" course is designed to equip digital marketers and e-commerce analysts with the knowledge and skills needed to effectively use generative artificial intellig.
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California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Simplified Chinese)

AWS ML Engineer Associate Curriculum Overview (Simplified Chinese) 在这个 AWS ML Engineer Associate Curriculum 的入门课程中，您将回顾机器学习 (ML) 基础知识并研究 ML 和 AI 的演变。您将探索 ML 生命周期的初始步骤，确定业务目标并根据该业务目标制定 ML 问题。最后，您将了解 Amazon SageMaker，这是一项完全托管式 AWS 服务，可用于.
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California Institute of Technology AWS Skill Builder

Desarrollo de soluciones de IA generativa (Español LATAM) | Developing Generative Artificial Intelligence Solutions (LATAM Spanish)

Desarrollo de soluciones de IA generativa (Español LATAM) | Developing Generative Artificial Intelligence Solutions (LATAM Spanish) En este curso, explorará el ciclo de vida de las aplicaciones de inteligencia artificial generativa (IA generativa), que incluye lo siguiente: Definición de un caso práctico empresarial.
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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!