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

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

3.1 Basics of Neural Networks Preview 3.2 Activation Functions and Their Role Preview 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

1. Understanding AI Agents 2. Case Studies 3. Hands-On Practice with AI Agents