This course provides a comprehensive introduction to AI programming. It covers fundamental AI concepts, machine learning (ML), deep learning (DL), and AI applications, using Python, TensorFlow, PyTorch, OpenCV, and Scikit-Learn. Students will learn AI development tools, build ML models, and implement deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The course explores AI applications in computer vision, natural language processing (NLP), GANs, and predictive modeling. Additionally, students will gain hands-on experience in AI deployment, using cloud computing (AWS, Azure, Google Cloud), web frameworks (Flask, Streamlit), and Edge AI (Raspberry Pi, Jetson Nano). Ethical considerations and AI’s future trends, such as quantum AI and AI in IoT, are also discussed. By the end of the course, students will develop and deploy real-world AI models, preparing them for careers in AI engineering, data science, and AI research. A final capstone project will showcase their knowledge in building AI-driven solutions. No prior AI experience is required, but basic Python skills are recommended.