In this course, you will use statistical machine learning techniques for performing classification to analyze data on customer "churn" (customers leaving a business) to gain insights into the characteristics of customers who are most likely to leave. Ideally, the results will enable the business to create a targeted campaign to retain these customers.
In Machine Learning 1 you'll be introduced to the basics needed for the entire sequence of courses.
- Using data mining tools to investigate patterns in complex data sets
- Preprocessing data for analytics
- Using decision tree classifiers to investigate classification problems
- Applying cross-validation methods
- Interpreting and drawing inferences from the results of data mining
- Assessing the predictive performance of classifiers by examining key error metrics
- Identifying where learning methods fail and gain insight into why with error analysis
- Drawing relationships between learner performance and measured features to help understand model performance
- Performing feature selection based on correlations between features in a dataset
This course will run from 1:00-3:00PM PDT Monday through Friday for two weeks. Course activities and class meetings are facilitated by industry experts who partner with UC Davis.