Deeplearning and AI

This course provides a comprehensive introduction to Artificial Intelligence (AI) and Deep Learning, focusing on the theory, algorithms, and practical applications used to build intelligent systems. Students will learn how machines can analyze data, recognize patterns, and make decisions using modern AI techniques.

The course begins with the fundamentals of machine learning, including supervised and unsupervised learning, and then moves into deep learning concepts such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer architectures. Learners will gain hands-on experience using popular frameworks like TensorFlow and PyTorch to build, train, and evaluate deep learning models.

Topics covered include computer vision, natural language processing, model optimization, and ethical considerations in AI development. By the end of the course, students will be able to design, implement, and deploy AI models to solve real-world problems.