Free, in-depth engineering courses. Learn by building, at your own pace.
The 2026-2027 practical track for Operating Systems 1, rebuilt around the current course plan: building Windows Forms applications in C#, file streams and directory operations, graphics and animation, threads and synchronization, CPU scheduling with a working simulator, installing and driving Ubuntu from the shell, Linux process management, and Bash scripting.
An introductory operating systems course built around hands-on practice: navigating and securing the Linux filesystem from the shell, understanding files as streams, and using C# to create and control processes, share memory across processes with memory-mapped files, and manage threads safely with locks and the thread pool.
A practical Operating Systems 2 course connecting classic OS concepts (process isolation, resource management, protection) to the two runtimes students actually build on: the Flutter mobile app runtime and the Docker containerization stack.
The 2026-2027 practical track for Machine Learning, built on the current course plan: advanced pandas and reading data from many sources, visualization with Matplotlib and Seaborn, linear and logistic regression, k-nearest neighbors, decision trees and random forests, naive Bayes, k-means clustering, content-based recommendations, sentiment analysis, and the final project. The labs run real Python code on real datasets, and each notebook has a link to open it in Colab.
A practical Arabic Natural Language Processing (NLP) course that builds from raw text to a working neural text classifier: Arabic corpora, tokenization and normalization, morphology, text representation, word embeddings, and sequence models.
A hands-on neural networks course built around TensorFlow and Keras: tensor fundamentals and automatic differentiation, linear regression via gradient descent, the perceptron and logic gates, the XOR problem and why depth matters, building a multi-layer network from scratch, the Keras Sequential and Functional APIs, convolutional neural networks for images, transfer learning, and recurrent networks (LSTM) for text classification.
A hands-on introduction to machine learning built from Mansoura University FCIS AI Department's Fall 2025 ML course labs, covering the end-to-end ML workflow, data preprocessing with pandas, unsupervised clustering (K-Means, Self-Organizing Maps, Fuzzy C-Means, DBSCAN) with proper evaluation, and supervised linear regression trained by both the normal equation and gradient descent in TensorFlow.
A practical software construction course covering clean code fundamentals, object-oriented design in Java, reference semantics and immutability, the Collections Framework, and a full testing curriculum spanning validation theory, black-box and white-box techniques, code coverage, JUnit 5, and exception handling and debugging.
A software-engineering course covering the Gang of Four design patterns, creational, structural, and behavioral, grounded in worked Python examples and a combined e-commerce case study, the common anti-patterns that undermine them, and core system-design fundamentals: scalability, load balancing, caching, database replication and sharding, and the CAP theorem, closing with a capstone system design that ties every concept together.
A hands-on introduction to ROS 1 (Melodic) for building robot software: what ROS is and why robotics teams standardize on it, installing it on Ubuntu, the master/node/topic communication model explored through the Turtlesim simulator, the anatomy of a catkin package, writing your own C++ publisher and subscriber nodes, and closing with an OpenCV-based perception pipeline (HOG pedestrian detection and YOLOv4 object detection and tracking) that mirrors how a real perception node feeds detections back into the ROS graph.
A hands-on ROS 2 (Jazzy Jalisco) course built around real robotics labs: setting up workspaces and packages with colcon, writing Python nodes with rclpy, mastering the publish/subscribe model, simulating a differential-drive robot in Gazebo, bridging simulation topics into ROS 2, building a custom keyboard teleop node, monitoring TurtleBot3 sensors and odometry in RViz, mapping an environment with SLAM Toolbox, and closing the loop with autonomous navigation using Nav2.
The 2026-2027 practical track for Modeling and Simulation: building simulation tables by hand with random numbers, simulating single-server queues, following how a computer simulation advances event by event and computes delay, queue length and server utilization, and generating random numbers and testing them for uniformity and independence.
