1.1 Introduction to AI
1.2 Core Concepts and Techniques in AI
1.3 Ethical Considerations

2.1 Overview of AI and its Various Applications
2.2 Introduction to AI Architecture
2.3 Understanding the AI Development Lifecycle
2.4 Hands-on: Setting up a Basic AI Environment

3.1 Basics of Neural Networks
3.2 Activation Functions and Their Role
3.3 Backpropagation and Optimization Algorithms
3.4 Hands-on: Building a Simple Neural Network Using a Deep Learning Framework

4.1 Introduction to Neural Networks in Image Processing
4.2 Neural Networks for Sequential Data
4.3 Practical Implementation of Neural Networks

5.1 Exploring Large Language Models
5.2 Popular Large Language Models
5.3 Practical Finetuning of Language Models
5.4 Hands-on: Practical Finetuning for Text Classification

6.1 Introduction to Generative Adversarial Networks (GANs)
6.2 Applications of Variational Autoencoders (VAEs)
6.3 Generating Realistic Data Using Generative Models
6.4 Hands-on: Implementing Generative Models for Image Synthesis

7.1 NLP in Real-world Scenarios
7.2 Attention Mechanisms and Practical Use of Transformers
7.3 In-depth Understanding of BERT for Practical NLP Tasks
7.4 Hands-on: Building Practical NLP Pipelines with Pretrained Models

8.1 Overview of Transfer Learning in AI
8.2 Transfer Learning Strategies and Techniques
8.3 Hands-on: Implementing Transfer Learning with Hugging Face Models for Various Tasks

9.1 Overview of GUI-based AI Applications
9.2 Web-based Framework
9.3 Desktop Application Framework

10.1 Communicating AI Results Effectively to Non-Technical Stakeholders
10.2 Building a Deployment Pipeline for AI Models
10.3 Developing Prototypes Based on Client Requirements
10.4 Hands-on: Deployment