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 Aalysis 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: Decison Tree ID3 Algorithm

  25. Modeling: Decision Tree ID3 Algorithm using RapdimIner

  26. Modeling: Decison 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 RapdimIner

12.99

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