Natural Language Processing: Techniques for processing and understanding human language, including text preprocessing, sentiment analysis, and language generation.
Computer Vision: Image and video analysis, object hongkong data detection, image classification, and convolutional neural networks for computer vision tasks.
Reinforcement Learning: Principles and algorithms for training agents to make decisions based on rewards and penalties.
AI Ethics and Responsible AI: Discussions on ethical considerations, bias, fairness, and privacy in AI development and deployment.
AI Applications: Case studies and projects applying AI techniques in various domains such as healthcare, finance, robotics, and recommendation systems.
popular AI tools, libraries, and frameworks like scikit-learn, Keras, and OpenCV.
Practical Implementation: Hands-on projects and assignments to apply AI concepts and techniques to real-world problems.