ENGG 6600 Cyber Security for Engineers

Welcome to the GitHub repository for the two-day introductory workshop on 'ENGG 6600 Cyber Security for Engineers.' This repository serves as a supplementary resource for beginners to get acquainted with Jupyter Notebooks and explore AI sample models, offering an accessible entry point into the world of machine learning.

Repository Structure
Getting Started
Contributing

Repository Structure

The repository is organized into folders and files as follows:

notebooks/: This folder contains Jupyter Notebook files (.ipynb) for various tutorials and sample models.
tutorials/: This folder holds Markdown files for tutorials. The tutorials are numbered for easy navigation (e.g., 00-Installing-Conda.md, 01-Introduction-to-Jupyter-Notebooks.md, etc.).

Getting Started

Prerequisites

Before you begin, make sure you have installed:

Git
Anaconda, Conda or Miniconda. (Search the differences and choose one)

Steps

Clone the Repository: Clone this repository to your local machine.

git clone https://github.com/threemmm/ENGG-6600-Cyber-Security-for-Engineers.git

Navigate to the Repository: Use the terminal to navigate to the directory where you cloned the repository.
```
cd ENGG-6600-Cyber-Security-for-Engineers
```
Understanding Environments: In the context of Python programming and data science, an "environment" refers to a named, isolated, working copy of Python that maintains its own files, directories, and paths. This allows you to work with specific versions of libraries and Python itself, without interfering with other projects.
- Create a New Environment: Create a new Conda environment and name it (replace your_env_name with the name you want).
```
conda create --name your_env_name python=3.8
```
- Viewing Conda Environments: To view the list of environments, run:
```
conda env list
```
- Activate the Environment: To activate the newly created environment, run:
```
conda activate your_env_name
```
- Accessing Environment in Jupyter: If you want to use this environment in Jupyter Notebook, you can install the ipykernel package and create a new kernel based on this environment.
```
conda install -n your_env_name ipykernel
python -m ipykernel install --user --name your_env_name --display-name "Python (your_env_name)"
```
  - The command python -m ipykernel install --user --name your_env_name --display-name "Python (your_env_name)" is used to install a Jupyter kernel associated with a specific Conda environment. Here's an academic explanation of each part of the command:
  - python: This part of the command invokes the Python interpreter.
  - -m ipykernel install: This part of the command uses the ipykernel module to install a new Jupyter kernel.
  - --user: This flag specifies that the kernel should be installed for the current user only. It places the kernel in the user's Jupyter directory, making it available for personal use.
  - --name your_env_name: This flag specifies the name of the Conda environment for which you want to create the Jupyter kernel. Replace your_env_name with the actual name of the Conda environment you want to use with Jupyter.
  - --display-name "Python (your_env_name)": This flag sets the display name for the Jupyter kernel. It's the name that will appear in the Jupyter Notebook interface when you choose a kernel for your notebook. The display name is typically set to "Python (your_env_name)" to indicate that this kernel is associated with the specified Conda environment.
Launch Jupyter Notebook: To start Jupyter Notebook, run:
```
jupyter notebook
```
This will open Jupyter Notebook in your web browser. Navigate to the notebooks/ folder to access the notebook files.

Contributing

We welcome contributions to improve the repository. Feel free to:

Report issues
Open pull requests
Suggest new features or tutorials

To contribute, please fork the repository and create a pull request with your changes. For accepted pull request contributions, a small bonus may be awarded as a token of appreciation

Thank you. Happy Learning!