Evaluating a Data Mining Model

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Janani Ravi

2:45:42

9 View

01 - Course Overview

01 - Course Overview.mp4

01:36

02 - Evaluating the Effectiveness of a Clustering Model

02 - Module Overview.mp4

01:41

03 - Prerequisites and Course Outline.mp4

01:21

04 - Module Summary .mp4

01:32

05 - Evaluating the Results of Data Mining.mp4

06:00

06 - White-box Models and Concept Drift.mp4

03:46

07 - Model Simplicity.mp4

05:13

08 - Evaluating Clustering Models .mp4

07:03

09 - Demo- Performing Clustering Analysis Using K-means Clustering.mp4

06:30

10 - Demo- Performing Clustering Analysis Using Agglomerative Clustering and Mean Shift Clustering.mp4

03:32

11 - Demo- Evaluating K-means Clustering Using Sum of Squared Distances and Silhoutte Score.mp4

05:25

12 - Demo- Evaluating Agglomerative Clustering and Estimating the Right Bandwidth for Mean Shift Clustering.mp4

04:35

03 - Evaluating the Effectiveness of Association Rule Mining

13 - Module Overview.mp4

01:45

14 - Association Rule Mining for Market Basket Analysis.mp4

02:45

15 - Support and Frequent Itemsets.mp4

04:02

16 - Confidence, Lift, and Conviction.mp4

08:00

17 - An Overview of the Apriori Algorithm.mp4

03:01

18 - Demo- Using the Apriori Algorithm to Generate Frequent Itemsets.mp4

06:59

19 - Demo- Association Rule Mining on a Toy Dataset.mp4

05:00

20 - Demo- Exploring the Bread Basket Dataset.mp4

03:42

21 - Demo- Association Rule Mining Using the Bread Basket Data.mp4

03:21

22 - Module Summary.mp4

01:19

04 - Evaluating the Effectiveness of Regression Models

23 - Module Overview.mp4

01:14

24 - Finding the Best Fit Line.mp4

02:37

25 - Interpreting Regression Results.mp4

03:29

26 - R-square and Adjusted R-square.mp4

02:47

27 - T-statistics and F-statistic.mp4

01:36

28 - Demo- Exploring the Regression Dataset.mp4

06:26

29 - Demo- Building and Evaluating a Regression Model.mp4

04:35

30 - Demo- Interpreting Results Using Residuals and Learning Curves.mp4

04:44

31 - Demo- Evaluating Multiple Regression Models.mp4

06:39

32 - Module Summary.mp4

01:24

05 - Evaluating the Effectiveness of Classification Models

33 - Module Overview.mp4

01:05

34 - Accuracy as an Evaluation Metric.mp4

02:07

35 - Module Summary.mp4

01:52

36 - Precision and Recall to Evaluate Classifiers.mp4

05:05

37 - The ROC Curve.mp4

04:49

38 - Validating Models Using Training, Validation, and Test Sets.mp4

05:17

39 - K-fold Cross Validation.mp4

03:13

40 - Demo- Exploring the Classification Dataset.mp4

04:46

41 - Demo- K-fold, Hold-out, and Shuffle Split Cross Validation.mp4

05:53

42 - Demo- Grid Search for Hyperparameter Tuning with Cross Validation.mp4

04:37

43 - Demo- Evaluating the Model Using Accuracy, Precision, Recall and the ROC Curve.mp4

03:19

Description

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.

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Data Mining

Janani Ravi

Instructor's Courses

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 high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.