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Course
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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.
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
- Version Check | 16s
- Module Overview | 1m 41s
- Prerequisites and Course Outline | 1m 21s
- Evaluating the Results of Data Mining | 6m
- White-box Models and Concept Drift | 3m 46s
- Model Simplicity | 5m 13s
- Evaluating Clustering Models | 7m 3s
- Demo: Performing Clustering Analysis Using K-means Clustering | 6m 30s
- Demo: Performing Clustering Analysis Using Agglomerative Clustering and Mean Shift Clustering | 3m 32s
- Demo: Evaluating K-means Clustering Using Sum of Squared Distances and Silhoutte Score | 5m 25s
- Demo: Evaluating Agglomerative Clustering and Estimating the Right Bandwidth for Mean Shift Clustering | 4m 35s
- Module Summary | 1m 32s
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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