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Generative AI courses

1062 Courses

California Institute of Technology Microsoft Learn

Enhance security operations by using Microsoft Security Copilot

Module 1: Discover common AI workloads with Azure AI services and Microsoft's Responsible AI policies. Learn the potential AI solutions and responsible AI practices. Module 2: Understand generative AI and large language models as the foundation of AI applications. Explore examples of copilots and effective prompts..
California Institute of Technology Coursera

GenAI for Customer Communication

Discover the power of AI in enhancing customer engagement and satisfaction with our course, "AI-Driven Customer Communication: Enhancing Engagement and Personalization." Learn practical techniques through personal case studies to deliver personalized, real-time interactions that foster loyalty. In this course, you'll explore foundational AI con.
California Institute of Technology edX

Inteligencia Artificial integrada a experiencias de aprendizaje

La inteligencia artificial ha tomado protagonismo en los últimos años, cambiando la manera en que aprendemos y trabajamos. Sectores como salud, finanzas, comercio y educación están viviendo esta revolución, lo que implica que los educadores deben actualizarse para implementar IA en sus prácticas. Este curso aborda conceptos fundamentales de.
California Institute of Technology FGV Educação Executiva

IA Generativa nas Aulas: Usando o Recurso a Nosso Favor

IA Generativa nas Aulas: Usando o Recurso a Nosso Favor é um curso voltado para professores que desejam modernizar suas práticas educativas através da integração da Inteligência Artificial. Oferecido pela FGV Educação Executiva, este curso ajuda os educadores a desenvolverem competências críticas e colaborativas fundamentais no ambiente de en.
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California Institute of Technology edX

GenAI for Execs & Business Leaders: Formulate Your Use Case

As an executive or business leader, harnessing the power of genAI within your organization is crucial.
California Institute of Technology edX

Mastering Generative AI: Model Foundations and NLP

The gen AI market is projected to grow by 42% CAGR by 2033 (Bloomberg). With natural language processing being key to this gen AI revolution, skilled data scientists and AI professionals are in high demand! If you’re aiming to break into the AI field, this IBM course on Generative AI - Model Foundations and NLP equips you with the skills employe.
California Institute of Technology edX

Mastering Generative AI: LLM Architecture & Data Preparation

The demand for generative AI is projected to grow over 46% annually by 2030, making this an opportune time for AI engineers, developers, data scientists, and machine learning professionals to enhance their skillsets. This course focuses on large language model (LLM) architecture and data preparation, providing the sought-after expertise employers d.
California Institute of Technology edX

Mastering Generative AI: Fine-Tuning Transformers

Mastering Generative AI: Fine-Tuning Transformers The demand for technical skills in generative AI is rapidly increasing. AI engineers with expertise in fine-tuning transformers for generative AI applications are highly sought after. This course, Generative AI Engineering Fine-Tuning with Transformers, is meticulously crafted for AI engineers and.
California Institute of Technology edX

Mastering Generative AI Project: RAG and LangChain App

Mastering Generative AI Project: RAG and LangChain App Are you eager to apply your generative AI engineering skills in a practical setting? This immersive project invites you to develop a real-world generative AI application, providing substantial material to discuss in interviews. Throughout the course, you'll expand your knowledge of L.
California Institute of Technology Coursera

Generative AI: Turbocharge Mobile App Development

Generative AI: Turbocharge Mobile App Development Embark on a comprehensive journey into the world of generative artificial intelligence (AI) with our course designed to revolutionize mobile app development. Discover how generative AI can be harnessed at every stage of the app development process, including design, content creation, marketing,.

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!