الدبلوم الاحترافي المتقدم في هندسة الذكاء الاصطناعي

This course provides the mathematical foundations required for advanced study in Artificial Intelligence. It covers linear algebra (vectors, matrices, eigenvalues), probability theory, statistics, and optimization techniques. Emphasis is placed on applying mathematical concepts to machine learning algorithms, including gradient-based optimization and probabilistic modeling.

This course develops advanced programming skills using Python for AI applications. Topics include data structures, object-oriented programming, and scientific computing libraries such as NumPy and Pandas. Students learn to implement algorithms efficiently and build scalable data-driven applications.

This course introduces fundamental concepts of machine learning, including supervised and unsupervised learning. Topics include regression, classification, clustering, and model evaluation techniques. Students gain hands-on experience in building predictive models and understanding their performance.

This course focuses on data preprocessing, exploratory data analysis (EDA), and visualization techniques. Students learn how to clean, transform, and interpret data using tools such as Matplotlib and Seaborn, enabling effective communication of insights.

This course equips students with the skills necessary to conduct scientific research in computing. Topics include research design, academic writing, literature review, citation standards, and ethical considerations in research.