This is the repository for the UC Berkeley D-Lab’s introducing to Machine Learning with a focus on Python's scikit-learn library.

Content outline:

• Overview
  • What is Machine Learning?
  • Types of Machine Learning algorithms
  • How to fit models
  • How to evaluate models
• Classification
• Regression
• Clustering
• Automatic Model Selection tool

Installation Requirements:

• python 3
• numpy
• matplotlib
• sklearn
• tpot
• jupyterlab

If you have a Berkeley CalNet ID, you can run these lessons on UC Berkeley's DataHub by clicking this link. By using this link, you can save your work and come back to it at any time. When you want to return to your saved work, just go straight to DataHub (https://datahub.berkeley.edu), sign in, and you click on the python-machine-learning folder.

If you don't have a Berkeley CalNet ID, you can still run these lessons in the cloud, by clicking this button: By using this button, you cannot save your work unfortunately.

If you are a Git user, simply clone this repository by opening a terminal and typing: git clone git@github.com:dlab-berkeley/python-machine-learning.git