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Evaluating a Data Mining Model

by Janani Ravi

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.

What you'll learn

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.

Table of contents

Course Overview

About the author

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more

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