Summary and Setup
In this section we will focus on setting up the computer for the Lesssons on Unsupervised Machine Learning.
Setting up virtual environment
In Python, the use of virtual environments allows you to avoid installing Python packages globally which could break system tools or other projects. Each virtual environment has its own Python binary (which matches the version of the binary that was used to create this environment) and can have its own independent set of installed Python packages in its site directories.
A virtual environment can be created by executing the command
venv in your Terminal (Mac OS and Unix) or at the command
line prompt (Windows):
python3 -m venv pyMLBy running this command, a new environment will be installed in your home directory.
The environment can be activated as:
source pyML/bin/activate Now the packages required for a specific purpose can be installed. E.g. for the Clustering lessons we need Pandas, Scikit-learn, Matplotlib, and a package called “Nibabel”:
pip3 install pandas scikit-learn matplotlib nibabelThis environment kernel needs to be added to your Jupyter notebook. This can be done as:
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=firstEnvAfter running these 2 commands, you will be able to select your
virtual environment from the Kernel tab of your Jupyter
notebook. More information can be accessed at this link.
Dataset
Dataset for this lesson includes: