In this module, you will learn to create a simple chatbot with open-source LLMs and integrate your chatbot into a web interface. You will explore the different components of the chatbot application and understand how a chatbot works. In addition, you will learn about selecting the right large language model or LLM for your chatbot. In this project, you will work with Facebook’s Blenderbot model and Hugging Face’s Python library, Transformers.

In this module, you will learn the basics of chatbots and their applications. You will set up a development environment to build a chatbot. You will build a chatbot that can take voice input and generate a spoken response using IBM Watson speech-to-text functionality and integrate with OpenAI’s GPT 3 model to incorporate high intelligence within the chatbot. And finally, you will learn to deploy the chatbot to a public server.

In this module, you will work on creating an app that captures audio using OpenAI Whisper and summarize it using Llama 2 LLM. Then, you will learn to deploy the app in a serverless environment using the IBM cloud code engine. This module provides a solid foundation for using LLMs for text generation and summarization tasks.

In this module, you will learn how large language models (LLMs) work and how to use them for data summarization and information extraction. You will work on a project to build your own chatbot that allows you to upload a PDF file and answer user queries based on the PDF. You will learn to use Llama 2 LLM supported by the Retrieval-augmented generation (RAG) technique. Finally, you will work with some of the popular frameworks like LangChain to make an intelligent chatbot.

In this module, you will acquire the necessary skills to create a voice translator assistant leveraging generative AI models like flan-ul2 and AI technologies like IBM Watson® Speech Libraries for Embed. This translator application will convert speech input to text and then provide the output through speech in a specified language. You will implement your Python, Flask, HTML, CSS, and JavaScript proficiency to create the web-based voice assistant.

In this module, you will create an AI career coach to help bridge the gap between talent and opportunity. As part of this AI Career Coach, you will build three applications: a resume enhancement tool, a personalized cover letter generator, and a career advisor. You will leverage the Llama-2-70b-chat large language model (LLM) integrated into the IBM watsonx.ai platform to build these applications. You will also leverage Gradio to build the web interface for these applications.