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Generative AI courses

1067 Courses

California Institute of Technology Coursera

GenAI for Sales Managers: Data-Driven Sales Forecasting

Welcome to this course GenAI for Sales Managers: Boosting Sales with GenAI focuses on equipping sales professionals with the knowledge and tools to integrate Generative AI (GenAI) into everyday sales workflows. From forecasting to customer engagement and pipeline management, this course shows you how to use GenAI, including ChatGPT and Claude AI, t.
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California Institute of Technology Coursera

GenAI for Sales Reps: Streamlining Lead Qualification

This course, GenAI for Sales Reps-Streamlining Lead Qualification, is designed to help sales professionals harness the power of Generative AI to automate and optimize lead qualification. You’ll start with the fundamentals of GenAI in sales, exploring how AI transforms lead scoring and prioritization. Then, you’ll learn to customize AI-driven models.
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California Institute of Technology edX

Use AI for Everyday Tasks

Use AI for Everyday Tasks" equips you with the foundational knowledge and practical skills to leverage AI in your daily workflow. You'll begin by understanding the core concepts of generative AI, including LLMs and NLPs, and explore their everyday applications, from smart assistants to AI agents. This course emphasizes responsible and effective AI.
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California Institute of Technology DataCamp

Understanding ChatGPT

Learn how to use ChatGPT. Discover best practices for writing prompts and explore common business use cases for the powerful AI tool. ChatGPT, by OpenAI, is a conversational language model that has gripped headlines worldwide, placing the power of AI at your fingertips. In this course, you’ll learn everything you need to know to begin using ChatG.
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California Institute of Technology Coursera

Generative AI: Empowering Modern Education

Discover how generative AI can help you create engaging, personalized learning content and improve your productivity so you can enhance student outcomes. During this course, you’ll explore gen AI methodologies and best practices for implementing AI in education. You’ll look at how it can be used to create content, enhance personalized learning, spe.
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California Institute of Technology Udemy

Generative AI, ChatGPT, Copilot & AI Agents Masterclass 2025

Master AI with an Expert: Automate Workflows, Build 12 Real-World Projects, & Unlock AI Agents, ChatGPT & CoPilot!
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California Institute of Technology FutureLearn

Artificial Intelligence for Healthcare

Delve into the transformative power of AI in the healthcare industry On this eight-week course, you’ll explore how AI is being used to turn the healthcare industry into a highly data-driven field. You’ll discover how different technologies are revolutionising areas in healthcare such as diagnostics, personalised medicine, and robotic surgery. With.
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California Institute of Technology FutureLearn

Artificial Intelligence for Everyone

Discover essential AI techniques with Universiti Malaya On this eight-week course from Universiti Malaya, you’ll gain a solid understanding of artificial intelligence. Tailored specifically for beginners, you’ll be guided through essential AI concepts such as machine learning, natural language processing, and computer vision. By the end, you’ll ha.
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California Institute of Technology Imperial College London

Mastering AI Leadership: Advanced Certificate in Prompt Engineering and Professional Ethics

In today's AI-driven world, technical prowess alone isn't enough. True mastery requires a deep understanding of both the tools and their societal implications. This unique two-course program integrates Prompt Engineering for ChatGPT and AI Ethics for Professionals, providing you with the skills to excel in generative AI while tackling its ethical.
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California Institute of Technology Coursera

Generative AI: Prompt Engineering Basics

This course provides a comprehensive introduction to Generative AI and Prompt Engineering, equipping learners with the skills to craft effective prompts that enhance AI-generated responses. Designed for professionals seeking to optimize AI interactions, the course covers fundamental AI concepts, the evolution of language models like GPT, BERT, and.
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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!