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 Project: AI Strategy Proposal for Your Business
Participants will develop an AI strategy proposal for a specific business problem, including use cases, tools, and implementation steps, based on their learnings throughout the course.
Learning Outcomes:
By the end of this course, participants will:
- Understand the fundamental concepts of AI and its applications in business.
- Be able to identify key AI technologies and their use cases.
- Learn how to use AI for decision-making, customer engagement, and operational efficiency.
- Gain practical insights on how to implement AI strategies in their organizations.
Curriculum
- 7 Sections
- 0 Lessons
- 8 Weeks
Expand all sectionsCollapse all sections
- 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 apps0
- 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 explained0
- 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 logging0
- 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 interactions0
- 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 loops0
- 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 feedback0
- Final Exam & CertificationPractical project evaluation Written assessment on key topics (language models, API integration, conversational design0
Requirements
- 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
