This course is designed for aspiring AI researchers, data scientists, and machine learning enthusiasts who want to understand and build state-of-the-art AI systems. You will start by mastering the essential mathematics for AI, including linear algebra, calculus, probability, and statistics, which form the backbone of neural network theory.

Next, you’ll dive into practical programming with PyTorch, learning how to implement neural networks, optimize models, and perform experiments. The course covers deep learning architectures, including feedforward networks, CNNs, RNNs, and advanced attention-based transformer models. You’ll also explore the principles behind large language models (LLMs), training strategies, and fine-tuning techniques.

Through hands-on projects, exercises, and tutorials, you will gain experience in building, training, and evaluating AI models. By the end of the course, you will have the technical expertise and conceptual understanding to contribute to AI research, implement AI solutions, and explore cutting-edge innovations in natural language processing and machine learning.