Introduction to Artificial Intelligence

  - Definition and evolution of AI

  - Differences between AI, Machine Learning, and Deep Learning

  - Overview of AI's impact across industries

Machine Learning Fundamentals

  - Introduction to key machine learning concepts

  - Types of learning: Supervised vs. Unsupervised Learning

  - Basic algorithms: Linear regression, classification techniques

 Deep Learning and Neural Networks

  - Understanding neural networks and their components

  - Introduction to deep learning techniques

  - Key architectures: CNNs and RNNs

 Natural Language Processing (NLP)

  - Basics of NLP and its significance

  - Key techniques: Tokenization, sentiment analysis

  - Introduction to advanced models: Transformers and GPT

AI in Robotics and Computer Vision

  - Role of AI in modern robotics

  - Fundamentals of computer vision and image processing

  - Key applications: Autonomous systems, facial recognition

 Ethics and Bias in AI

  - Ethical implications of AI technologies

  - Understanding and addressing bias in AI models

  - Overview of AI governance and regulation

 AI Applications in Industry

  - Case studies of AI in healthcare, finance, and retail

  - Challenges and success stories from AI implementations

  - Future trends in AI across various sectors

 Future Trends and Course Summary

  - Emerging AI technologies: Explainable AI, AI at the edge

  - Open discussion on AI’s future impact

  - Summary of key concepts and course wrap-up