| Summary: | In this course, you will learn how to interact and create applications with Generative AI APIs like OpenAI. We will cover how to set up GitHub Codespaces with GitHub Copilot, learn about DALL-E and use it to generate images from text prompts, learn how to fine tune LLM models to a specific use case and check for accuracy to avoid hallucinations. You will learn the capabilities of the Whisper API for encoding and summarizing audio and video files. We will walk through an example of ingesting a speech audio file, getting the transcript from OpenAI, and then getting a summary of the key points back. This provides the basis for applications like meeting or video summarizers. Additionally we will cover the rapidly growing GenAI market, key partnerships and acquisitions happening in the space, as well as important principles around AI ethics and safety when developing AI applications. Set up and use GitHub Codespaces and Copilot to optimize programming Understand and use the DALL-E API to generate images from text Fine tune OpenAI models to a specific use case (ex. Reddit Q&A bot) Use the Whisper API to transcribe and summarize audio and video files Get an understanding of the fast-growing GenAI market and key players Learn AI ethics principles for developing responsible AI applications. Setting up your development environment with GitHub Codespaces and Copilot Using the DALL-E API to generate images from recipe text and shopping lists Data preparation and fine-tuning an OpenAI model on Reddit Q&A data Using the fine-tuned model as a Reddit Q&A assistant Transcribing and summarizing audio/video with the Whisper API Overview of the GenAI market size, growth projections, and key partnerships Important AI ethics principles to consider when developing AI applications
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