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.
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 45m
Table of contents
Course Overview
Evaluating the Effectiveness of a Clustering Model
Evaluating the Effectiveness of Association Rule Mining
Evaluating the Effectiveness of Regression Models
Evaluating the Effectiveness of Classification Models
Description
Course info
Level
Beginner
Updated
Oct 29, 2019
Duration
2h 45m
Description

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.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Evaluating a Data Mining Model. A little about myself, I have a masters degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. 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, 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'll 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.