An Introduction to Machine Learning with Scikit-Learn
Please install the dependencies for this repository using pip or conda as follows:
$ pip install -U -r requirements.txt
or
$ conda install -f requirements.txt
Note that you may want to install these requirements in a virtualenv or conda environment in order to make dependency management simpler.
After you've installed the dependencies, simply open a Jupyter notebook:
$ jupyter notebook
If you're here from a cohort before Spring 2020, please refer to the archive folder to find any notebooks that may have been demonstrated during your courses.
The demos folder contains live code exercises by date. If you're looking for code that was written during class, look for the course date in this folder.
The examples folder contains work submitted by students engaged in the UCI machine learning repository. Please feel free to submit a PR with your work in this folder!
The notebooks folder has some example notebooks to refer to when working on the UCI machine learning lab or working with scikit-learn. In particular:
- estimators.ipynb - a tour of Scikit-Learn from the perspective of the API
- wheat.ipynb - an example classification notebook