Module 1: Foundations of Artificial Intelligence

1.1 Introduction to AI

1.2 Types of Artificial Intelligence

1.3 Branches of Artificial Intelligence

1.4 Applications and Business Use Cases

Module 2: Mathematical Concepts for AI

2.1 Linear Algebra

2.2 Calculus

2.3 Probability & Statistics

2.4 Discrete Mathematics

Module 3: Python for Developer

3.1 Python fundamentals

3.2 Python Libraries

Module 4: Mastering Machine Learning

4.1 Introduction to Machine Learning

4.2 Supervised Machine Learning Algorithms

4.3 Unsupervised Machine Learning Algorithms

4.4 Model Evaluation and Selection

Module 5: Deep Learning

5.1 Neural Networks

5.2 Convolutional Neural Networks (CNNs)

5.3 Recurrent Neural Networks (RNNs)

Module 6: Computer Vision

6.1 Image Processing Basics

6.2 Object Detection

6.3 Image Segmentation

6.4 Generative Adversarial Networks (GANs)

Module 7: Natural Language Processing

7.1 Text Preprocessing and Representation

7.2 Text Classification

7.3 Named Entity Recognition (NER)

7.4 Question Answering (QA)

Module 8: Reinforcement Learning

8.1 Introduction to Reinforcement Learning

8.2 Q-Learning and Deep Q-Networks (DQNs)

8.3 Policy Gradient Methods

Module 9: Cloud Computing in AI Development

9.1 Cloud Computing for AI

9.2 Cloud-Based Machine Learning Services

Module 10: Large Language Models

10.1 Understanding LLMs

10.2 Text Generation and Translation

10.3 Question Answering and Knowledge Extraction

Module 11: Cutting-Edge AI Research

11.1 Neuro-Symbolic AI

11.2 Explainable AI (XAI)

11.3 Federated Learning

11.4 Meta-Learning and Few-Shot Learning

Module 12: AI Communication and Documentation

12.1 Communicating AI Projects

12.2 Documenting AI Systems

12.3 Ethical Considerations