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Generative AI courses

659 Courses

California Institute of Technology Codecademy

Differentiate for Language and Reading Level With Generative AI Case Study

Event Title: Differentiate for Language and Reading Level With Generative AI Case Study Description: Harness the power of generative AI to produce text that caters to a wide range of reading and language levels. This case study will guide you in utilizing AI as a tool to generate diverse educational materials. Whether working with a provided s.
California Institute of Technology Codecademy

Pair Programming with Generative AI Case Study

Pair Programming with Generative AI Case Study Use generative AI for pair programming by teaming up with an AI system to generate Python code in the driver-navigator style of pair programming. In this case study, you will develop your skills in using generative AI for pair programming. Taking the mindset of a novice developer, you will team up w.
California Institute of Technology Pluralsight

The IT Ops Sessions: Performance Troubleshooting with Generative AI and Wireshark

In this IT Ops session, discover how to leverage generative AI to analyze packet captures from Wireshark. The IT Ops sessions offer short-form, conference-style learning without the need for an actual conference. During the session titled "Performance Troubleshooting with Generative AI and Wireshark", you'll explore how tools like ChatGPT and B.
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California Institute of Technology Pluralsight

Streamline Data Queries with LangChain

Title: Streamline Data Queries with LangChain Description: This course will teach you LangChain query essentials, optimizing performance for swift data retrieval, and advanced techniques such as nested queries, joins, and troubleshooting strategies. LangChain is a popular framework that has recently seen rapid adoption for building generative.
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California Institute of Technology Coursera

GenAI for Data Analysis : OpenAI Assistant API

GenAI for Data Analysis: OpenAI Assistant API In this 2-hour long project-based course, you will learn how to create an AI Assistant using the OpenAI API. You have been contacted by ToyTrends, an online retail store, to develop an advanced AI assistant capable of conducting insightful data analysis on their sales data. ToyTrends has supplied you.
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California Institute of Technology Pluralsight

Generative AI Techniques for Social Engineering

Generative AI Techniques for Social Engineering This course will teach you to learn to think like both an attacker and defender, using advanced GenAI techniques to outsmart social engineering threats. Ideal for analysts, developers, and admins looking to elevate their cybersecurity game. Ever wondered how to stay ahead of crafty soci.
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California Institute of Technology AWS Skill Builder

AWS Cloud Quest: Generative AI

AWS Cloud Quest: Generative AI Unlock the potential of generative AI with the AWS Cloud Quest: Generative AI course offered by AWS Skill Builder. Dive into an extensive range of AWS services including Amazon S3, AWS Lambda, Amazon SageMaker, Amazon EC2, Amazon API Gateway, Amazon CloudFront, Amazon DynamoDB, AWS Cloud 9, Amazon OpenSearch Servic.
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California Institute of Technology Pluralsight

Copilot for Web

Copilot for Web Course: Copilot for Web Description: Copilot for Web is like having a research assistant, personal planner, and creative partner by your side whenever you browse the internet. This course will teach you how to leverage Copilot to get answers, create images, and boost your productivity. Microsoft’s generative AI assistant is avail.
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California Institute of Technology Pluralsight

How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock

How to Add GenAI Capabilities to Your App Code Using Amazon Bedrock Get started with Amazon Bedrock and learn how to integrate it with your app. Amazon Bedrock is the easiest way to build and scale generative AI applications with foundational models. In this demo, Principal AWS Training Architect, Faye Ellis, explains how to use this fully man.
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California Institute of Technology Coursera

GenAI For Business Analysis: Fine-Tuning LLMs

Title: GenAI For Business Analysis: Fine-Tuning LLMs Description: In this 2-hour project, you'll learn how to fine-tune the GPT-3.5 model using the OpenAI API in Python. You are an AI engineer employed by PulseNet, a telecommunications company that provides internet, television, and phone services. PulseNet operates with a large customer base.
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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!