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This project aims to develop an Intrusion Detection System (IDS) 🕵️♂️🕵️♂️ using deep learning techniques. The IDS model is designed to detect malicious activities and potential threats within a network by analyzing traffic data.
This is a collection of all the machine learning techniques required in any machine learning project. It contains detailed descriptions, videos, book recommendations, and additional material to properly grasp all the concepts.
The blockCV package creates spatially or environmentally separated training and testing folds for cross-validation to provide a robust error estimation in spatially structured environments. See
The aim of this project is to develop a machine learning model to predict the levels of CO in the air using historical datasets containing atmospheric variables. The project makes use of variables selection, decision trees, and cross-validation techniques to ensure robustness and model accuracy.