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Generative AI courses

1063 Courses

California Institute of Technology DataCamp

Cleaning Data with Generative AI

Struggling with messy datasets that hinder your data analysis? Discover how generative AI can revolutionize data cleaning by addressing duplicates, nulls, and inconsistent formatting. We invite individuals new to data analytics to engage with a chatbot designed to streamline the data cleaning process. Gain hands-on experience by enhancing y.
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California Institute of Technology New York University (NYU)

AI in Digital Marketing

Course Title: AI in Digital Marketing Institution: New York University (NYU) Provided by: Coursera Course Overview: AI in Digital Marketing is crafted to arm digital marketers, brand strategists, and business leaders with the crucial AI tools and techniques needed today. Participants will delve into AI's vital role in boosting customer engag.
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California Institute of Technology Coursera

GenAI for Financial Insights and Analysis with QUILL

Discover the power of generative AI in financial analysis with the course "GenAI for Financial Insights and Analysis with QUILL." This cutting-edge course is perfect for financial professionals, investors, and analysts aiming to leverage AI for deep insights into financial data. Learn to transform complex datasets into actionable insights and.
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California Institute of Technology University of California, Davis

Writing with Generative AI

Join the University of California, Davis on Coursera for an engaging course that demystifies the world of Generative AI. Across four comprehensive modules, learn to navigate the technological landscape and maximize AI's potential effectively. In Module 1, explore generative AI and Large Language Models (LLMs), gaining the skills to harness.
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California Institute of Technology Coursera

GenAI for Customer Insights

Unlock the potential of generative AI with the "Gen AI for Customer Insights" course. Dive into the transformative world of AI as you analyze customer data to reveal patterns, predict trends, and extract actionable insights. Through practical examples and interactive learning, this course will enrich your decision-making capabilities, improv.
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California Institute of Technology Coursera

GenAI for Contract Drafting Basics

Revamp your contract drafting process with the 'GenAI for Contract Drafting Basics' course. Targeted at legal professionals, this course offers essential insights into using Generative AI for creating precise and complex contracts at unprecedented speeds. Enhance your knowledge of legal drafting fundamentals and AI's practical applications to.
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California Institute of Technology Coursera

Implementation of GenAI Agents

Immerse yourself in the dynamic world of AI agents with our intensive course, crafted for aspiring AI architects and innovators. Over 75 minutes, acquire the expertise to build AI agents equipped to understand, reason, and function within various real-world contexts. This course prioritizes efficiency and practical learning, offering direct cod.
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California Institute of Technology Udemy

The AI Playbook for CEOs, Business Owners and Leaders

Unveil the potential of Generative AI and ChatGPT with Udemy's expertly crafted course, "The AI Playbook for CEOs, Business Owners and Leaders." This program offers strategic insights and practical applications essential for realizing the business value of AI in today's competitive landscape. Created for those in leadership and business roles.
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California Institute of Technology edX

Mastering Generative AI for Data Analytics

Presently, 82% of global companies are embracing AI within their operations, marking generative AI skills as crucial for career advancement. Stay ahead by leveraging the power of generative AI in data analytics. This course, tailored for both budding and veteran data analysts, demands a basic understanding of data analytics, prompt engineering, P.
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California Institute of Technology edX

Generative AI and its Applications in Finance

Generative AI and its Applications in Finance Generative AI (GAI) is a powerful technology that enables computers to create new content, such as text, images, or financial data, by learning from existing patterns. Unlike traditional AI, which analyses and processes data, GAI leverages advanced machine learning models to generate realistic outputs.
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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!