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Course Outline
Introduction
Installing and Configuring RapidMiner
Overview of RapidMiner Studio Interface and Mechanics
Recap of the Analytical Cycle
Overview of Repository
Importing Data
Preparing Data
Modeling
Validation
Using Macros
Using Global Search
Buidling More Sophisticated Predictive Models
Evaluating Model Quality
Troubleshooting and Optimization
Summary and Conclusion
Requirements
- An understanding of data science concepts
Testimonials
He was very informative and helpful.
Pratheep Ravy
the scope of material
Maciej Jonczyk
systematizing knowledge in the field of ML
Orange Polska
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
The remote classroom setting worked very well
- Trimac Management Services LP
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
The many practical examples / assignments that we went through were great. For me, I learn better by seeing examples and applying them elsewhere. The use of real data and applying what was taught against it was extremely valuable. Michaels PowerPoint presentations and his ability to work through each solution was invaluable.
- Trimac Management Services LP
The exercises.
Elena Velkova - CEED Bulgaria
Practical exercises with R were very helpful.
CEED Bulgaria
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