Download the Python 3 Anaconda installer for your OS and open the Anaconda prompt.

Clone this repo using git or download it manually. Then, using the Anaconda prompt, cd into the ml-tutorial directory and move on to [Starting the tutorial](#Starting the tutorial).

Clone the repo and create a virtual environment in the root folder. After activating the environment, install the required packages:

    pip install -r requirements.txt

Open the release/1/index.ipynb Jupyter notebook with the following command:

    jupyter notebook release/1/index.ipynb

Activate the nbgrader notebook extension as documented here. Change the notebooks in the source/ folder, any new cells where students have to write code should be set to Autograded answer as documented here.

After saving your changes, run:

    sh generate_assignment.sh

This will overwrite the existing release/ directory.

While creating the notebooks in the release/ directory, the nbgrader package will alter code segments that look like:

    ### BEGIN SOLUTION
    foo = bar
    ### END SOLUTION

into:

    ##### Implement this part of the code #####
    raise NotImplementedError("Code not implemented, follow the instructions.")

As specified in the nbgrader_config.py file.

If you want to host this workshop you can use the included Dockerfile to build a JupyterHub which you can build and launch with the following commands:

docker build . -t ml-tutorial

docker run -p 8000:80 ml-tutorial

Surf to http://localhost:8000 to start the tutorial and log in with user001 and password password.

The docker image is also available on docker hub so running it on a server should work with: docker run -d -p 80:80 pbutenee/ml-tutorial without the need for downloading the code or building the container. Set the port to 8000 instead of 80 to test it out on local host. NOTE: ONLY AN OLD VERSION OF THIS CONTAINER IS AVAILABLE ON DOCKER HUB SINCE IT IS NO LONGER FREE