Evaluating a Data Mining Model

This course covers the important techniques in model evaluation for some of the most popular types of data mining techniques. These techniques range from association rules learning to clustering, regression, and classification.

Data Mining is an umbrella term used for techniques that find patterns in large datasets. Thus, data mining can effectively be thought of as the application of machine learning techniques to big data. In this course, Evaluating a Data Mining Model, you will gain the ability to answer the two most important questions that every practitioner of data mining must answer - is a particular model valid for this data? And, if yes, what is that model telling us? First, you will learn that evaluating model fit and interpreting model results are key steps in the data mining process. Next, you will discover how association rules learning - a classic data mining technique - is implemented and evaluated. Finally, you will round out your knowledge by seeing how the popular ML solution techniques - regression, classification, and clustering - can be implemented and evaluated for fit. When you’re finished with this course, you will have the skills and knowledge to implement data mining techniques, evaluate them for model fit, and then intelligently interpret their findings.