By Thomas J. Fan
Scikit-learn is a machine learning library in Python that is used by many data science practitioners. Machine learning is a valuable tool used across many domains such as medicine, physics, and finance. We will start this training by learning about scikit-learn’s API for supervised machine learning. scikit-learn's API mainly consists of three methods: fit, to build models, predict, to make predictions from models, and transform, to change the representation of the input data. This simple and consistent interface helps to abstract away the algorithm, thus allowing us to focus on our particular problems.
The most convenient way to download the material is with git:
git clone https://github.com/thomasjpfan/ml-workshop-introPlease note that I may add and improve the material until shortly before the session. You can update your copy by running:
git pull origin masterIf you are not familiar with git, you can download this repository as a zip file at: github.com/thomasjpfan/ml-workshop-intro/archive/master.zip. Please note that I may add and improve the material until shortly before the session. To update your copy please re-download the material a day before the session.
Local installation requires conda to be installed on your machine. The simplest way to install conda is to install miniconda by using an installer for your operating system provided at docs.conda.io/en/latest/miniconda.html. After conda is installed, navigate to this repository on your local machine:
cd ml-workshop-introThen download and install the dependencies:
conda env create -f environment.ymlThis will create a virtual environment named ml-workshop-intro. To activate this environment:
conda activate ml-workshop-introFinally, to start jupyterlab run:
jupyter labThis should open a browser window with the jupterlab interface.
If you have any issues with installing conda or running jupyter on your local computer, then you can run the notebooks on Google's Colab:
This repo is under the MIT License.