TDWI Chicago

Level: Beginner to Intermediate

Prerequisite: None

Deanne Larson, Ph.D.

DM, CBIP

President

Larson & Associates

Python is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using Python. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be leveraged in building Python machine learning skills.

You Will Learn

• How to use linear models such as linear regression and logistic regression
• How to choose features and complete feature reduction
• How to use clustering models such as k-means
• How to do time series forecasting
• How to use classification models such as decision trees and k-nearest neighbor
• How to use ensemble methods
• How to do model validation

Geared To

• Those that have foundational knowledge of Python and machine learning and are looking to expand their knowledge and apply best practices

Laptop Setup

Students will use their own computers to complete lab exercises. Installation of Python and select open source libraries will be required in advance of the course.

Installation instructions will be emailed to registrants prior to the event. You must prepare your computer BEFORE the event.

There is no time allotted in class for computer preparation..

* Enrollment is limited to 35 attendees.