Building Artificial Intelligence Apps with ChatGPT

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  • Level
  • Duration
  • Certificate
  • All Levels
  • 8 Weeks (Flexible based on course intensity)
  • Bharat Academy Education
  • Certified
  • - KHDA
    - AIAL
    - Royal Institute for Chartered Engineer - RICE (USA)
Course Info
Key Highlights
Target Audience
Course Overview

This course provides an in-depth guide to creating AI-driven applications using ChatGPT. Participants will learn how to integrate OpenAI's ChatGPT API into various applications, design conversational agents, and customize AI responses for specific business needs. The course covers foundational AI concepts, practical coding tutorials, and real-world use cases, making it ideal for developers and professionals looking to harness AI for business automation, customer service, or innovative app solutions.

Final Exam & Certification
  • Practical project evaluation
  • Written assessment on key topics (language models, API integration, conversational design
Learning Outcomes
  • Gain hands-on experience in building AI-powered applications using ChatGPT.
  • Understand how to integrate AI into real-world apps and services.
  • Learn how to optimize conversational AI for specific use cases.
  • Be able to deploy and manage AI applications in production environments.
Tools & Technologies
  • Languages/Frameworks: Python, Flask/Django, HTML/CSS, JavaScript
  • APIs: OpenAI API, third-party APIs for data integration
  • Tools: Postman, GitHub, AWS/Heroku for deployment
Module 1: Introduction to AI and ChatGPT (Week 1)
1.1 What is Artificial Intelligence (AI)?
    • History and evolution of AI
    • AI in real-world applications
1.2 Understanding Language Models
    • Evolution of NLP (Natural Language Processing)
    • GPT architecture overview (Generative Pre-Trained Transformer)
    • ChatGPT: What it is and how it works
1.3 ChatGPT Use Cases
    • Customer service, Virtual assistants, Content generation
    • Case studies of successful AI-powered apps
Module 2: Setting Up the Development Environment (Week 2)
2.1 Prerequisites
    • Overview of Python, APIs, and libraries needed for the course
    • AI in real-world applications
2.2 Setting Up a Python Environment
    • Installing Python and essential packages (e.g., OpenAI, Flask, Django)
2.3 Working with the OpenAI API
    • Registering and accessing OpenAI's API
    • API key management and best practices
2.4 First API Call to ChatGPT
    • Using the OpenAI library to make basic API calls
    • Parameters and response types explained
Module 3: Building Simple Chat Applications (Week 3-4)
3.1 Introduction to Conversational AI Design
    • User experience design for AI-powered conversations
    • Creating user intents and responses
3.2 Building a Simple Chat Interface
    • Designing a basic frontend (HTML, CSS, JS)
    • Connecting the backend to ChatGPT API using Flask/Django
3.3 Adding Chat Features
    • Context management and session tracking
    • Generating dynamic responses
    • Handling multiple conversations in real-time
3.4 Testing and Debugging
    • How to test AI responses
    • Error handling and logging
Module 4: Enhancing ChatGPT Apps with AI Capabilities (Week 5-6)
4.1 Integrating External Data Sources
    • How to feed real-time data into ChatGPT responses (APIs, databases)
4.2 Customizing AI Responses
    • Fine-tuning GPT models with custom datasets
    • Setting personality and tone for the chatbot
4.3 Building a Smart Assistant
    • Integrating calendars, to-do lists, and smart notifications
    • Connecting to external APIs (Google Calendar, weather APIs, etc.)
4.4 Handling Complex Dialogues
    • Creating multi-turn conversations
    • Managing conversation context across various interactions
Module 5: Advanced Topics (Week 7)
5.1 Integrating Speech-to-Text & Text-to-Speech
    • Using tools like Google Cloud Speech or Azure Speech API
    • Voice-enabled AI chat applications
5.2 Deploying ChatGPT Apps
    • Hosting options (AWS, Heroku, etc.)
    • Best practices for production deployments
    • Security considerations for AI applications
5.3 Monitoring and Analytics
    • Setting up analytics to track user interactions and AI performance
    • Improving AI accuracy through feedback loops
Module 6: Building a Full AI App (Week 8)
6.1 Capstone Project Overview
    • Students choose a project idea (e.g., customer service chatbot, personal assistant)
    • Guidance on structuring the project
6.2 Developing the AI App
    • Implementing all learned concepts in a full AI application
6.3 Presenting Your AI App
    • Presentations and feedback from peers/instructors
    • Improving and refining the project based on feedback
  •  
  • Developers with basic programming knowledge
  • AI enthusiasts
  • Entrepreneurs looking to integrate AI into their products
  • Data scientists interested in conversational AI
  • Product managers looking to understand AI integration

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