Code-Repository für den Kurs "Einführung in das Maschinelle Lernen" an der Hochschule Karlsruhe.

The recommended way to setup your development environment is to use Miniconda:

Download and install Miniconda from here

On Windows, start the application Anaconda Prompt. On Mac or Linux, just open a new terminal window.

The following steps are now similar in both OS:

1. Create a new environment for this course:

   conda create --name ml-course python=3.8

2. Activate this environment:

   conda activate ml-course

3. Install the following packages: numpy, pandas, matplotlib, scikit-learn.

   conda install numpy pandas matplotlib scikit-learn jupyter seaborn

4. Test your installation by opening a new jupyter notebook. A new browser window should open.

   jupyter notebook

1. Go to https://github.com/pabair/ml-kurs-ss26

2. Copy the URL of the new Repository

3. In a Terminal, run: git clone [URL of repo]

4. Change into the directory: cd ml-kurs-ss26

5. (later) As the original repository gets updated, run git pull in the terminal to fetch the changes from Github to your computer

If you don't want to use Conda, you can also install the packages using pip in a virtual environment:

1. Create a new virtual environment:

   python3 -m venv venv/

2. Activate the environment:

   source venv/bin/activate

3. Install the packages:

   pip install -r requirements.txt

4. Start the Jupyter Notebook with jupyter notebook .

Instead of jupyter notebook, you can also open the project in VSCode (with the Jupyter extension installed) by running code . in the terminal and then selecting the venv as the Python interpreter for Jupyter.