Developing Generative AI Applications with Python

This module introduces students to generative AI and foundation models, with a focus on image captioning applications. Students learn to use Hugging Face's BLIP model and Gradio framework to build and deploy an image captioning application. The module includes hands-on experience with Code Engine for deployment.

This module focuses on building a chatbot-enabled website using Python and Flask. Students learn to integrate open-source LLMs from Hugging Face into a web application, creating a ChatGPT-like interface. The module covers both backend chatbot development and frontend web integration.

This module teaches students how to build a voice assistant using Docker, OpenAI's GPT-3, and IBM Watson's speech services. Students learn to implement speech-to-text and text-to-speech capabilities, creating an interactive voice-based AI assistant.

This module covers the development of an AI meeting assistant using IBM watsonx.ai and Meta's Llama 2. Students learn to implement OpenAI Whisper for speech processing and create a comprehensive business meeting companion application.

This module focuses on using LangChain and Retrieval-Augmented Generation (RAG) technology with Llama 2 to create data-specific chatbots. Students learn to enhance LLM accuracy by incorporating private data sources and implementing RAG techniques.

This module teaches students to create a language translation system combining Large Language Models with Speech-to-Text and Text-to-Speech technologies. The project involves building a comprehensive translation tool that can handle both text and voice input/output.