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Training
Course Information
Course Name
AI-NT: Agentic AI for Non-Technical People
Duration
2 Days
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Overview
Overview
Overview
Audience Profile
Prerequisities
At Course Completion
Course Outline
Course Outline
Module 1: Welcome and Introduction
- Overview of course objectives and structure
- Introduction to AI and its relevance in today’s world
Module 2: Understanding Artificial Intelligence
- Definition and types of AI
- History and evolution of AI
- Applications of AI in various industries
Module 3: Core Concepts of AI
- Data and its importance in AI
- Machine Learning vs. Traditional Programming
- Key terminologies (e.g., algorithm, model, training, testing)
Module 4: Types of AI and Machine Learning
- Narrow AI vs. General AI
- Supervised, Unsupervised, and Reinforcement Learning
Module 5: Artificial Neural Networks (ANN)
- Basic concepts of ANNs
- Structure and functioning (neurons, layers, weights)
- Real-world examples of ANN applications
Module 6: Interactive Session: Q&A and Group Discussions
- Addressing common misconceptions about AI
- Discussing participants’ thoughts and experiences with AI
Module 7: AI in Everyday Life
- AI in healthcare, finance, transportation, and more
- Demonstrations of AI-powered applications and tools
Module 8: AI Tools and Technologies
- Overview of popular AI tools (e.g., chatbots, image recognition, virtual assistants)
- Hands-on activity: Exploring an AI tool (e.g., interacting with a chatbot)
Module 9: Building a Simple AI Model: MNIST Digit Classifier
- Introduction to the MNIST dataset
- How digit classification works
- Demonstration: Training a simple ANN to classify MNIST digits
- Interpreting the results and understanding model accuracy
Module 10: AI Project Lifecycle
- Steps involved in developing an AI project
- Importance of data collection and preprocessing
- Model building, evaluation, and deployment
Module 11: Building a Simple Weather Chatbot
- Introduction to the NLP
- Introduction to python NLP modules (NLTK and spacy)
- Demonstration: Simple Weather Chatbot
Module 12: Ethical and Social Implications of AI
- Bias in AI and how to mitigate it
- Privacy concerns and data protection
- The impact of AI on jobs and society
Module 13: Future of AI
- Emerging trends in AI research and development
- Potential future applications of AI
- Preparing for an AI-driven future
