Python for Data Analysis: Machine Learning with Python

Tuesday, April 26, 2022, 12:00pm to 1:20pm
Wednesday, April 27, 2022, 12:00pm to 1:20pm
Thursday, April 28, 2022, 12:00pm to 1:20pm
Online venue
University of Iowa, Iowa City, IA 52242

1. Overview

Python is a powerful general-purpose programming language. This 3-session workshop introduces the practical applications of machine learning, primarily using the Python package scikit-learn. The workshop is taught using Jupyter Notebook in the Interactive Data Analytics Service (IDAS).

2. Prerequisites

Participants are expected to be familiar with Python and Jupyter Notebook. Theoretical (mathematical) knowledge of machine learning concepts is not required but may be helpful.

3. Eligibility

This workshop is available to current University of Iowa members only

4. How to register

Click here then log in with your HawkID and password. Click “Register now” at the bottom of the page to register. After registering successfully, an automated email with a Zoom link will be sent to your University of Iowa email. Registration will close at 11 am on Monday, April 25, 2022.

5. Additional information         

If you have any questions, please see the workshop FAQs or contact

6. Workshop agenda

This workshop is taught in 3 sessions over 3 days. Each session builds on the previous ones. Participants are encouraged to attend all sessions in order to learn the complete contents of the workshop.

This is not a theoretical (mathematical) introduction to machine learning, nor is it a comprehensive introduction to all machine learning algorithms. The workshop focuses on the practical aspects of using Python for machine learning, primarily with the package scikit-learn. If you are already familiar with the concepts below, please see the workshop FAQs for a list of additional, free learning resources.

Tentative topics to be covered:

Day 1

  • Overview of categories of machine learning
  • Introducing scikit-learn, a Python package commonly used for machine learning
  • Supervised learning – Regression

Day 2

  • Supervised learning – Regression (continued)
  • Supervised learning – Classification

Day 3:

  • Supervised learning – Classification (continued)
  • Unsupervised learning – Clustering
Individuals with disabilities are encouraged to attend all University of Iowa–sponsored events. If you are a person with a disability who requires a reasonable accommodation in order to participate in this program, please contact Giang Rudderham in advance at 319-353-5982 or