Data Mining with RapidMiner

Data Mining with RapidMiner.

This is the bite size course to learn Data Mining using Rapidminer. This course uses CRISP DM data mining process.

You will learn RapidMiner to do data understanding, data preparation, modeling, Evaluation. You will be able to train your own prediction models with naive bayes, decision tree, knn, neural network, linear regression, and evaluate your models very soon after learning the course.

You can take the course as follow and you can take an exam at EMHAcademy to get SVBook Advance Certificate in Data Science using DSTK, Excel, RapidMiner:

– Introduction to Data and Text Mining using DSTK 3

– Data Mining with RapidMiner

– Learn Microsoft Excel Basics Fast

– Learn Data Analysis using Microsoft Excel Basics Fast.

Content

1. Getting Started
2. Getting Started 2
3. Data Mining Process
4. Download Data Set
5. Read CSV
6. Data Understanding: Statistics
7. Data Understanding: Scatterplot
8. Data Understanding: Line
9. Data Understanding: Bar
10. Data Understanding: Histogram
11. Data Understanding: BoxPlot
12. Data Understanding: Pie
13. Data Understanding: Scatterplot Matrix
14. Data Preparation: Normalization
15. Data Preparation: Replace Missing Values
16. Data Preparation: Remove Duplicates
17. Data Preparation: Detect Outlier
18. Modeling: Simple Linear Regression
19. Modeling: SImple Linear Regression using RapidMiner
20. Modeling: KMeans Clustering
21. Modeling: KMeans Clustering using Rapidminer
22. Modeling: Agglomeration Clustering
23. Modeling: Agglomeration Clustering using Rapidminer
24. Modeling: Decision Tree ID3 Algorithm
25. Modeling: Decision Tree ID3 Algorithm using RapdimIner
26. Modeling: Decision Tree ID3 Algorithm using RapidMiner
27. Evaluation: Decsion Tree ID3 Algorithm using Rapidminer
28. Modeling: KNN Classification
29. Modeling: KNN Classification using Rapidminer
30. Evaluation: KNN Classification using Rapidminer
31. Modeling Naive Bayes Classification
32. Modeling: Naive Bayes Classification using Rapidminer
33. Evaluation: Naive Bayes Classification using Rapidminer
34. Modeling: Neural Network Classification
35. Modeling: Neural Network Classification using Rapidminer
36. Evauation: Neural Network Classification using Rapidminer
37. What Algorithm to Use?
38. Model Evaluation
39. k fold cross validation using Rapidminer

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