ATC-GENAIF - Introduction to Generative AI - Foundational


Duration: 1.0 day

This course is designed to empower participants from diverse backgrounds with a comprehensive understanding of the technology underpinning Generative AI. Exploring frequently encountered terms such as Artificial Intelligence, Machine Learning, and Deep Learning, participants will gain a heightened comprehension of what these technologies entail and where and how they are applied. Tailored for individuals with varying technical and non-technical backgrounds, this course offers an ideal learning experience for all.


  • This course aims to equip participants with a holistic grasp of the foundational principles of Generative AI technology. By delving into commonly used terminologies like Artificial Intelligence, Machine Learning, and Deep Learning, participants will achieve an enhanced understanding of the core concepts and applications within these domains.
  • Develop a comprehensive understanding of the technology that drives Generative AI, bridging the knowledge gap for participants from diverse backgrounds.
  • Demystify frequently encountered terms such as Artificial Intelligence, Machine Learning, and Deep Learning, elucidating their roles and significance within the broader context of Generative AI.
  • Gain insights into the practical applications of Generative AI and its relationship with other key technologies. Understand where and how these technologies are applied in real-world scenarios.
  • Elevate participants' comprehension levels, enabling them to navigate through the intricate landscape of Generative AI with confidence.


Module 1. Introduction to Course

  • Welcome
  • GenerativeAI in action
  • Case Study: Generative AI
  • Understanding AI/ ML / DL / GenAI

Module 2. Introduction to Artificial Intelligence

  • Introduction to AI
  • Definition & Scope
  • Types of Artificial Intelligence
  • Strong AI vs Weak AI
  • Narrow AI vs General AI
  • Benefits of using AI
  • Limitations & General Use cases
  • Case Study

Demo: Amazon Rekognition

Lab - Build Chatbot with Amazon Lex

Module 3. Upgrading with Machine Learning

  • What is Machine Learning?
  • Definition and Scope of Machine Learning
  • Historical Development of Machine Learning
  • Types of Machine Learning
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Machine Learning Pipeline
  • Case Study

Module 4: Adding power of Deep Learning

  • Introduction to Deep Learning
  • Understanding Deep Neural Networks
  • Applications of Deep Learning
  • Layers in Neural Networks
  • Types of Neural Network
  • Feedforward Neural Network
  • Recurrent Neural Networks (RNNs)
  • Convolutional Neural Networks (CNNs)
  • Long Short-Term Memory Network

Module 5: Engaging with Generative AI

  • What is Generative AI
  • Types of Generative AI
  • Foundational Models
  • Large Language Models
  • Applications of GenerativeAI
  • Challenges & Considerations
  • Amazon Rekognition
  • Google BERD
  • Chat GPT
  • Azure CoPilot

Module 6: Cloud Overview

  • Introduction to Cloud: aws/gcp/azure
  • Scaling & Deploying on Cloud
  • Securing GenerativeAI Application on Cloud

Module 7: Risk of Generative AI

  • Understanding risk
  • Setting up rules




No prerequisites



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