Welcome to the Machine Learning for Networking repository. This project is part of a university course designed to integrate machine learning techniques into the domain of computer networks. The repository contains exercises and laboratory work aimed at deepening understanding and practical application of ML concepts in the context of networking.
This repository is organized into the following main folders:
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Laboratories/: This directory contains all the laboratory projects and assignments, focusing on the practical implementation of machine learning models in networking scenarios. These labs are designed to help students build hands-on skills and explore real-world data and networking challenges.
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Exercises/: This folder includes various exercises related to machine learning and its application in network analysis. The exercises are structured to reinforce theoretical knowledge and test students' understanding through problem-solving.
The Machine Learning for Networking course covers topics that bridge the gap between machine learning and network engineering. Students will learn how to:
- Apply supervised and unsupervised learning to network data.
- Build predictive models to enhance network performance.
- Detect anomalies and optimize network traffic using ML algorithms.
- Work with datasets relevant to network behavior and performance metrics.
To get started with this repository:
- Clone the repository to your local machine:
https://github.com/RenatoMignone/Machine-Learning-for-Networking