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When & Where
Date: 
Fri, November 1, 2019 - 9:00 AM to 12:00 PM
Location: 
Barrows 356: D-Lab Convening Room
Description
Type: 

This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets.

Prior knowledge: We will assume a basic knowledge of Python and a basic understanding of machine learning techniques. No theory instruction will be provided.

Tech requirement: Laptop required. Please make sure Python 3 is installed, as well as the following packages:

  • scikit-learn
  • jupyter
  • tpot
Primary Tool: 
Python
Details
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 
Laptop

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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