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Machine Learning for Network and Cloud Engineers

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# ml4nce
Machine Learning for Network and Cloud Engineers

![image](https://user-images.githubusercontent.com/76206935/215793827-60062668-8e0e-4132-aa3b-8bee29114897.png)

https://www.amazon.com/Machine-Learning-Network-Cloud-Engineers-ebook/dp/B0BT6YZC33/ref=sr_1_1?crid=1QA5Z38TPI925&keywords=javier+antich&qid=1675176585&sprefix=javier+antich%2Caps%2C197&sr=8-1

This repository contains:
- Notebooks and datasets for the use cases described in the book.
- SANDS: Situation Aware Network Data Simulator: data generation tool to practice with machine learning algorithms in network and cloud data

Use case notebook execution instructions:
- I recommend you to use Google Colab (https://colab.research.google.com/). The notebooks have been built using Colab. Libaries installation, etc., will work seamlessly in Colab. If you run it in your own Jupyter Notebook environment, take into account libraries and software/hardware compatibilities.
- Upload the use case files to your Google Drive.
- Open the notebook (double-click on the .ipynb file).
- The notebooks refer to paths that are important to find the right files. Examples:

sys.path.append('/content/drive/MyDrive/Colab Notebooks/final/SANDS')
    
    path_files ='/content/drive/MyDrive/Colab Notebooks/final/UC1'
    
- Either create the referred folders in your gdrive and place the use case files and subfolders in the referred path, or modify the paths to accomodate the location where you have uploaded the use case files in your gdrive. Either way should work.
- Follow the instructions in the book to run and play with the use case notebook.

For any feedback: javier.antich@gmail.com
Thanks!

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