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jadijadi/machine_learning_with_python_jadi
The notebooks we use on ML course
Jupyter notebooks from a Persian-language ML course hosted on Maktabkhooneh, covering the standard supervised/unsupervised learning curriculum: regression, classification, clustering, and basic recommender systems. It's a companion repo to a video course, not a standalone learning resource.
Covers a solid breadth of classical ML algorithms in one place — linear/polynomial regression, SVM, decision trees, KNN, K-Means, DBSCAN, hierarchical clustering, and both content-based and collaborative filtering. Each notebook is self-contained with its own dataset included, so you can run them without hunting for data. The naming convention (ML0101EN-*) mirrors IBM's ML0101EN course structure, meaning the notebooks are familiar to anyone who's taken that curriculum.
Entirely a course companion — without the video lectures, many notebooks lack explanatory text and read as bare code cells. No deep learning coverage at all: no neural nets, no PyTorch or TensorFlow, nothing past sklearn. The ratings.csv truncation is a real issue for the recommender system notebooks since the collaborative filtering results will differ from what the course videos show. Last meaningful update appears to be 2021; nothing here reflects the last four years of the sklearn API or ecosystem changes.