Building Generative AI-Powered Applications with Python

Building Generative AI-Powered Applications with Python

This course is part of IBM AI Developer Professional Certificate

Instructors: IBM Skills Network Team

Sponsored by University of Texas at Austin

9,294 already enrolled

7 modules

Gain insight into a topic and learn the fundamentals.

4.8

(65 reviews)

Intermediate level

Recommended experience

13 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

7 modules

Gain insight into a topic and learn the fundamentals.

4.8

(65 reviews)

Intermediate level

Recommended experience

13 hours to complete

3 weeks at 4 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Explain the core concepts of generative AI models, AI technologies, and AI platforms such as IBM watsonx and Hugging Face.
Integrate and enhance large language models (LLMs) using RAG technology to infuse intelligence into apps and chatbots.
Utilize Python libraries like Flask and Gradio to create web applications that interact with generative AI models.
Build generative AI-powered applications and chatbots using generative AI models, Python, and related frameworks.

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Assessments

7 assignments

Taught in English

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This course is part of the IBM AI Developer Professional Certificate

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There are 7 modules in this course

Ready for an interactive learning experience to develop applications and chatbots for diverse use cases using generative AI?

This course provides an opportunity to work on guided projects that provide step-by-step instructions to build generative AI-powered applications. You'll utilize Python, along with related libraries like Flask and Gradio, and frameworks such as Langchain. In the course, you will work on hands-on projects to build chatbots and apps by utilizing popular large language models (LLMs) such as GPT-3 and Llama 2, hosted on platforms such as IBM watsonx and Hugging Face. Additionally, you'll explore retrieval-augmented generation (RAG) technology, enhancing LLMs by incorporating external information beyond their training data. This course also equips you to build voice-enabled chatbots and apps using IBM Watson® Speech Libraries for Embed. To develop these projects, you'll be using Python, making it essential to have a basic understanding of the language. While knowing some HTML, CSS, and JavaScript can be beneficial, it's not a requirement. The course includes supporting videos and readings to build a foundational understanding of models, frameworks, and technologies used in the projects.

In this module, you will learn the basics of generative AI models and explore the AI models and data sets using the Hugging Face platform. You will work on a guided project that involves image captioning using Python, the BLIP model, and Gradio. This project will let you build an automated image caption tool using generative AI and implement it for real-world scenarios.

What's included

5 videos2 readings1 assignment2 app items3 plugins

5 videosTotal 25 minutes

Course Introduction4 minutesPreview module
Generative AI Models 6 minutes
Foundation Models6 minutes
Project Overview: Image Captioning with Generative AI3 minutes
Hugging Face5 minutes

2 readingsTotal 5 minutes

Course Syllabus and Prerequisites3 minutes
Module Summary: Image Captioning with Generative AI2 minutes

1 assignmentTotal 21 minutes

Module 1 Graded Quiz: Image Captioning with Generative AI21 minutes

2 app itemsTotal 75 minutes

Lab: Give Meaningful Names to Your Photos with IMG Captioning AI60 minutes
[Optional] Lab: Deploy your App with Code Engine15 minutes

3 pluginsTotal 33 minutes

Helpful Tips for Course Completion3 minutes
Reading: BLIP from Hugging Face Transformers15 minutes
Reading: Introduction to Gradio15 minutes

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.

What's included

1 video1 reading1 assignment2 app items1 plugin

1 videoTotal 4 minutes

Project Overview: Create Your Own ChatGPT-like Website4 minutesPreview module

1 readingTotal 2 minutes

Module Summary: Create Your Own ChatGPT-Like Website2 minutes

1 assignmentTotal 21 minutes

Module 2 Graded Quiz: Create Your Own ChatGPT-like Website 21 minutes

2 app itemsTotal 90 minutes

Lab: Create Simple Chatbot with Open Source LLMs using Python and Hugging Face60 minutes
Lab: Integrating Your Chatbot into a Web Application30 minutes

1 pluginTotal 15 minutes

Reading: Flask: A Gateway to Web Development in Python15 minutes

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.

What's included

2 videos1 reading1 assignment1 app item1 plugin

2 videosTotal 8 minutes

Project Overview: Create a Voice Assistant4 minutesPreview module
Introduction to Docker3 minutes

1 readingTotal 1 minute

Module Summary: Create a Voice Assistant1 minute

1 assignmentTotal 21 minutes

Module 3 Graded Quiz: Create a Voice Assistant21 minutes

1 app itemTotal 60 minutes

Lab: Create a Voice Assistant with OpenAI's GPT-3 and IBM Watson60 minutes

1 pluginTotal 15 minutes

Reading: IBM Watson Speech-to-Text and Text-to-Speech15 minutes

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.

What's included

2 videos1 reading1 assignment1 app item2 plugins

2 videosTotal 8 minutes

Project Overview: Generative AI-Powered Meeting Assistant3 minutesPreview module
IBM watsonx.ai5 minutes

1 readingTotal 1 minute

Module Summary: Generative AI-Powered Meeting Assistant1 minute

1 assignmentTotal 21 minutes

Module 4 Graded Quiz: Generative AI-Powered Meeting Assistant 21 minutes

1 app itemTotal 60 minutes

Lab: Business AI Meeting Companion60 minutes

2 pluginsTotal 30 minutes

Reading: Introduction to Meta Llama 2 15 minutes
Reading: Introduction to OpenAI Whisper 15 minutes

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.

What's included

3 videos1 reading1 assignment1 app item1 plugin

3 videosTotal 14 minutes

Project Overview: Summarize Your Private Data with Generative AI & RAG4 minutesPreview module
Introduction to LangChain3 minutes
Enhancing LLM Accuracy with RAG6 minutes

1 readingTotal 2 minutes

Module Summary: Summarize Your Private Data with Generative AI 2 minutes

1 assignmentTotal 21 minutes

Module 5 Graded Quiz: Summarize Your Private Data with Generative AI 21 minutes

1 app itemTotal 60 minutes

Lab: Build a Chatbot for Your Data60 minutes

1 pluginTotal 15 minutes

Reading: Introduction to Llama 2 and RAG 15 minutes

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.

What's included

1 video3 readings1 assignment1 app item1 plugin

1 videoTotal 3 minutes

Introduction to Project: Babel Fish with LLM and STT TTS3 minutesPreview module

3 readingsTotal 6 minutes

Module Summary: Babel Fish with LLM and STT TTS2 minutes
Congratulations and Next Steps3 minutes
Thanks from the Course Team1 minute

1 assignmentTotal 21 minutes

Module 6 Graded Quiz: Babel Fish with LLM and STT TTS21 minutes

1 app itemTotal 90 minutes

Lab: Babel Fish (Language Translator) with LLM, STT, & TTS90 minutes

1 pluginTotal 5 minutes

Glossary: Building GenAI- Powered Apps with Python5 minutes

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.