• Business professionals looking to leverage AI for operational efficiency without a technical background.
• Educators and trainers interested in incorporating AI tools into their curriculum.
• Tech enthusiasts who want to explore AI applications without diving into coding.

• Basic computer literacy.
• Familiarity with data concepts (e.g., data tables, basic statistics).
• Interest in AI applications across various domains.
• No coding experience required; suitable for beginners

Course Outline:

Module 1: AI Fundamentals

• Overview: Grasp core concepts of AI and understand different types including Machine Learning, Natural Language Processing, and Computer Vision.
• Responsible AI: Learn about ethical practices and responsible AI development.
• Tools:
  • Introduction to zero-coding AI platforms like Google AutoML, Microsoft Azure Machine Learning Studio, and Amazon SageMaker Canvas.

Module 2: Zero-Coding Platforms

• User-Friendly AI Tools: Discover AI tools with drag-and-drop interfaces.
• Hands-On Experience: Build a simple AI model using:
  • Google AutoML for image recognition or text classification.
  • Microsoft Azure Machine Learning Studio for creating and deploying machine learning models.
  • Amazon SageMaker Canvas for constructing machine learning workflows with pre-built components.

Module 3: Real-World Impact of Zero-Coding AI

• Chatbots: Explore the use of zero-coding AI in developing chatbots.
• Image Recognition: Understand how zero-coding AI tools can be used for image recognition tasks.
• Predictive Analytics: Learn about the application of zero-coding AI in predictive analytics.

Module 4: Practical Applications Showcase

• Case Studies: Analyze examples of zero-coding AI in action across different industries.
• Individual Projects: Work on individual projects to apply zero-coding AI tools in real-world scenarios.

Practical Tools to be Used

• Google AutoML: A user-friendly interface for building AI models for image recognition, text classification, and time series forecasting.
• Microsoft Azure Machine Learning Studio: A drag-and-drop environment for creating and deploying machine learning models without coding knowledge.
• Amazon SageMaker Canvas: A visual interface for building and deploying machine learning workflows with pre-built components.