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Generative AI courses

659 Courses

California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Indonesian)

Exploring Artificial Intelligence Use Cases and Applications (Indonesian) Dalam kursus ini, Anda akan menjelajahi kasus penggunaan kecerdasan buatan (AI), machine learning (ML), dan kecerdasan buatan generatif (AI generatif) di dunia nyata di berbagai industri. Area-area ini meliputi pelayanan kesehatan, keuangan, marketing, hiburan, dan banyak l.
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California Institute of Technology Coursera

Introduction to Generative AI for Data Analysis

Welcome to the Introduction to Generative AI for Data Analysis, a foundational course designed to provide you with a comprehensive understanding of generative AI and its practical applications in the field of data analysis. Throughout this course, you will: Define generative AI and understand its pivotal role in data analysis. Explor.
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California Institute of Technology Coursera

Coding and Automation for Data Analysis with Generative AI

Embark on a transformative journey with our course on Coding and Automation for Data Analysis with Generative AI. Designed for aspiring data analysts and seasoned professionals alike, this course will guide you through using AI-powered tools for optimized code generation. Focus on mastering SQL, Python, and R for efficient data analysis as you.
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California Institute of Technology Coursera

Scenario and Root Cause Analysis with Generative AI

Join our comprehensive course on Scenario and Root Cause Analysis with Generative AI offered by Coursera. This program is designed to equip you with the skills to apply generative AI in scenario planning and root cause analysis. Throughout the course, you will: Conduct scenario analysis using advanced generative AI models Perform root c.
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California Institute of Technology Coursera

Advanced Data Analysis with Generative AI

Join the course 'Advanced Data Analysis with Generative AI' to explore sophisticated analytical methods using AI. You'll gain hands-on experience with predictive modeling, time-series forecasting, and anomaly detection to uncover patterns in complex datasets. This course also covers text data analysis, which allows you to derive insights from un.
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California Institute of Technology University of Michigan

GenAI in Business: Planning Framework for Implementation

GenAI in Business: Planning Framework for Implementation - University of Michigan | Coursera Join the University of Michigan's 'GenAI in Business: Planning Framework for Implementation' course on Coursera, an insightful journey into the planning phase of AI implementation in business. This course delves into the "Plan" stage of the "See, Plan,.
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California Institute of Technology University of Michigan

GenAI in Business: Discover the Possibilities

Embark on the first course in the Generative AI in Business series, where you'll be introduced to the "See" phase of the See, Plan, Act framework. This course offers an in-depth exploration of how generative AI is revolutionizing industries by automating routine tasks and enhancing customer interactions and strategic insights. Through an ins.
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California Institute of Technology University of Michigan

GenAI in Business: Strategies for Successful Execution

In the final installment of the 'Generative AI in Business' series, this course focuses on the critical 'Act' phase of the 'See, Plan, Act' framework. Dive deep into a structured, five-step methodology to build, launch, and scale the generative AI solution you've meticulously crafted. Analyze what drives the long-term success of your GenAI p.
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California Institute of Technology AWS Skill Builder

AWS Flash - AWS AI/ML Essentials (GCR Only)

This targeted course is designed for technicians aiming to pass the AWS Certified AI Practitioner (AIF-C01) exam, helping you understand the exam process and key topics. Enhance your mastery of essential AI and ML knowledge, and become familiar with exam formats and difficulty through sample questions. Levels: Intermediate Teaching method.
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California Institute of Technology AWS Skill Builder

AWS Flash – Cost Optimization Solutions for FinOps (Part 2)

This course delves into cost optimization strategies for popular AWS services and workloads. Participants will learn how to identify cost-saving opportunities through rightsizing, reserved capacity utilization, and leveraging serverless technologies. Discover techniques for managing data transfer costs across AWS services and regions, and get.
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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!