1. Introduction to AI

• Understanding AI: This module provides a fundamental introduction to the field of AI, its history, definition, goals, and different types of AI based on functionality and capabilities (narrow AI, general AI, super AI).
• Applications of AI: Exploring the impact and use of AI across diverse industries like healthcare, finance, retail, and transportation, helps learners appreciate the scope of AI's real-world relevance.  

2. Foundational skills

• Mathematics for AI: Learning the basics of linear algebra, calculus, probability, and statistics is essential for comprehending how AI algorithms work, especially in machine learning and deep learning.
• Programming for AI: Familiarity with a popular language like Python is crucial for developing and implementing AI algorithms and models.
• Data Structures and Algorithms: Understanding how to efficiently store, retrieve, and manipulate data using common data structures (arrays, trees, lists) and algorithms is vital for creating effective AI applications.
• Data Manipulation and Preprocessing: AI models depend on high-quality data. Therefore, the ability to clean, transform, and prepare datasets is a key skill. Libraries like Pandas are often used for this purpose. 

3. Core AI concepts and techniques

• Machine Learning (ML): This module explores how machines learn from data without explicit programming, including supervised, unsupervised, and reinforcement learning, and key algorithms such as Decision Trees and Regression.
• Deep Learning (DL) and Neural Networks: This section focuses on using multi-layered neural networks for processing data, including understanding neural network architecture and specific types like CNNs and RNNs.
• Natural Language Processing (NLP): NLP covers the interaction between computers and human language, allowing machines to understand, interpret, and generate text, with techniques like sentiment analysis and language models.
• Computer Vision (CV): CV enables machines to interpret visual information, including techniques like image recognition and object detection.
• Generative AI: This area focuses on models that can create new content based on learned patterns. 

4. AI ethics and societal implications

• AI Ethics: This involves addressing ethical considerations in AI development, such as fairness, bias, transparency, and accountability.
• Bias Detection and Mitigation: Learning how to identify and correct biases in AI models.
• Transparency and Explainability: Understanding the importance of designing AI systems that are transparent and explainable. 

5. Practical experience and projects

• Hands-on Practice with AI Tools and Frameworks: Gaining practical experience with libraries like TensorFlow and PyTorch.
• Real-world Projects: Applying learned concepts to build projects like chatbots or image classifiers.