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Code-Free Machine Learning: Introduction to Concepts and Best Practices with Hands-on Experiences

Course Date: September 7 ~ October 5

Days: W (10:00am – 12:00pm)

Social scientists are increasingly interested in machine learning methods to glean scientific knowledge and actionable insights from designed and gathered data. Implementing machine learning, however, requires users to have programming skills. This can be a daunting challenge for many non-tech savvy researchers. This course aims to guide social scientists to explore how machine learning can be used for their research without learning how to code. This course uses a graphical user interface tool Orange to provide learners with hands-on experiences in implementing machine learning techniques including data cleaning, visualization, and fine-tuning of algorithmic models. The open-source tool Orange is built on popular Python packages, providing basically the same functions and performances as many data scientists would obtain by writing complicated code. The course demonstrates that researchers can utilize the power of machine learning without learning how to code and focus more on machine learning concepts and best practices as well as analytical model development and validation.

1 course hour
Instructor: Jinseok Kim
Prerequisite: You must have your own laptop or desktop with Orange installed to participate in this class. For installation instruction of Orange, see https://orangedatamining.com/
Location: remote