Machine Learning and its Applications in Market Research

Part 2: Predictive analytics using ML frameworks in Python
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Type On demand
Digital Yes


13:00-15:00 UTC / 15:00-17:00 CET - Two hours per day over three days.

This course builds on the previous course Python for Market Research. It introduces you to the topic of Machine Learning (ML), how to build predictive models using ML algorithms and to use them for inferencing, decision making and forecasting. Data pre-processing will be an integral part of this course.

The course will focus on learning by doing and so be prepared for hands-on work and exercises. Introductory videos will be made available to help the participant ease into the course.

This course is a follow-up to the prerequisite course:
Python for Market Research. Use code VA_Python25_FDP when buying both for a 25% discount!

This course will cover the following:

  • Data pre-processing

  • Machine Learning using Scikit-Learn

  • Building predictive models

  • Applications in Market Research and hands-on exercises

After this course, you’ll be able to:

  • Use different data pre-processing techniques for different kinds of data

  • Use the ML frameworks in Python like Scikit-Learn for model building

  • Work with different ML algorithms to build predictive models based on data

  • Measure and analyze mode accuracy and tweak models for better performance

  • Discuss applications of ML methods in Market Research

Who should attend?

Anyone who has attended the Python for Market Research course or anyone with intermediate-level programming knowledge in Python and interested in learning Machine Learning methods.

Prerequisites for the course are:

  • Complete the intro course Python for Market Research OR

  • Has an intermediate level of Python programming knowledge

  • High school-level mathematics (Algebra, etc.)


Jag Rao
Professor of Practice at University of Georgia