Generative AI courses

1067 Courses

Udemy

Chatbot Creation with Generative AI: A Practical Guide

From Basics to Advanced: Mastering Chatbot Development with Generative AI Tools

AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (Español LATAM)

Después del 28 de marzo, los títulos de los cursos solo estarán disponibles en inglés. Sin embargo, las descripciones de los cursos permanecerán disponibles en su idioma preferido para que pueda consultarlas.

AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Español LATAM)

AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (Português)

Depois de 28 de março, os títulos dos cursos estarão somente em inglês. No entanto, as descrições dos cursos permanecerão disponíveis no idioma de sua preferência para permitir que você pesquise nesse idioma.

AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Português)

AWS Skill Builder

Responsible Artificial Intelligence Practices (Português)

AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Español LATAM)

AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Português)

Southern New Hampshire University

AI Agent Developer

In this Specialization, you’ll gain hands-on experience developing AI agents using Python, OpenAI tools, and prompt engineering techniques. You’ll learn to design agent architectures, implement tool use and memory, build custom GPTs, and apply best practices for responsible, trustworthy AI. By the end, you’ll be able to create and deploy intelligen.

Southern New Hampshire University

Generative AI Data Analyst

This Specialization equips you with the skills to become a Generative AI Data Analyst, using ChatGPT to streamline data analysis, automate repetitive tasks, and uncover insights faster. You’ll learn to work with spreadsheets, databases, and unstructured documents—transforming raw data into compelling stories and visualizations. By mastering these A.

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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:

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.

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.

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.

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.

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!