Introduction to Artificial Intelligence 

• Overview of AI and its significance 

• History and evolution of AI 
• Types of AI: Narrow AI vs. General AI
   

Mathematics for AI

• Basics of linear algebra and calculus 

• Probability and statistics in AI 

• Mathematical foundations of machine learning
  

Machine Learning Fundamentals 

• Supervised vs. unsupervised learning 

• Key algorithms (e.g., linear regression, decision trees) 

• Model evaluation and performance metrics

Deep Learning

• Introduction to neural networks 

• Understanding deep learning architectures (e.g., CNNs, RNNs) 

• Practical applications of deep learning

Natural Language Processing (NLP)

• Basics of NLP and its applications 

• Text processing techniques and algorithms 

• Sentiment analysis and language models

Computer Vision

• Introduction to computer vision and image processing 

• Object detection and recognition techniques 

• Practical applications in real-world scenarios

AI Ethics and Responsible AI

• Understanding ethical implications of AI 

• Bias in AI systems and how to address it 
• Regulatory considerations in AI development