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