Artificial Intelligence

Course Content

ntroduction to Artificial Intelligence Definition and history of AI Basic concepts and terminology AI applications across industries Machine Learning Foundations Supervised learning, unsupervised learning, and reinforcement learning Regression, classification, and clustering algorithms Model evaluation and validation techniques Neural Networks and Deep Learning Fundamentals of neural networks Deep learning architectures (CNNs, RNNs, etc.) Training neural networks and hyperparameter tuning Natural Language Processing (NLP) Text preprocessing and tokenization Word embeddings and vector representations Sentiment analysis, text classification, and language generation Computer Vision Image preprocessing and feature extraction Convolutional Neural Networks (CNNs) for image classification and object detection Applications of computer vision in industry and research Reinforcement Learning Markov Decision Processes (MDPs) Q-learning, policy gradients, and actor-critic methods Applications in robotics, gaming, and autonomous systems Ethics and Bias in AI Understanding ethical considerations in AI development and deployment Addressing bias and fairness issues in AI algorithms Regulatory frameworks and guidelines for responsible AI usage AI Applications and Case Studies Real-world examples of AI implementation in various domains (healthcare, finance, e-commerce, etc.) Hands-on projects and case studies to apply AI techniques learned throughout the course Future Trends in AI Emerging technologies and trends in AI research Ethical and societal implications of advanced AI technologies Opportunities and challenges in the future of AI