Arabsera course library
Learn the foundations. Then build beyond them.
Clear, rigorous courses in mathematics, computer science, data, and AI, taught by experienced academics and practitioners.
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SubscribeArabsera describes its teaching approach as explanations in Arabic and English, with written materials primarily in English. Check individual course materials and sample lessons for details.
01Data Structures in C
Learn fundamental data structures and develop the skills to design new structures for specific applications.
02Probability
Study probability through mathematical proofs, intuitive explanations, real-life applications, and datasets.
03Linear Algebra, Part I
Learn the foundations of linear algebra through a balance of mathematical rigor and intuitive understanding.
04Machine Learning, Part I
Understand the foundations of regression and classification and analyze real-life datasets.
05Discrete Mathematics, Part I
Develop rigorous problem-solving and mathematical thinking through logic, proofs, sets, and functions.
06Digital Design
Combine theory, Verilog, and laboratory experiments to design and analyze digital circuits.
07Optimization, Part I
Build mathematical and intuitive understanding of convex sets, functions, and optimization problems.
08Data Science: Metro-rider Snapshots
Survey probability, statistics, linear algebra, visualization, and machine learning foundations using Python.
09Mathematical Statistics, Part I
Build on probability to study sampling, point estimation, bias, consistency, and efficiency.
10Mathematical Statistics II
Build on Mathematical Statistics I to study hypothesis testing, likelihood-ratio tests, confidence intervals, and goodness of fit.
11Software Requirements Engineering Using LLMs
Learn requirements engineering and use large language models to develop user stories and UML diagrams.
12Machine Learning Applications in Healthcare
Apply machine learning to clinical data, patient risk, diagnosis, prognosis, medical text, and imaging.
13AI Engineering
Learn to build production AI systems and end-to-end machine learning training and inference workflows.