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Rapidminer Essentials

This course will teach you from basics to advanced features of RapidMiner as quickly as possible as well as working Big Data

Course Objective

RapidMiner provides an extensible, highly scalable set of tools to unifies the entire data science lifecycle from data prep to machine learning to predictive model deployment. This course will teach you from basics to advanced features of RapidMiner as quickly as possible as well as working Big Data. This 5 (five) days course is designed for anyone who wants to perform data modelling, text and web mining.

 

What will you learn?

  • About RapidMiner
  • Build and run reusable RapidMiner processes
  • Construct analytical model
  • Connect to RapidMiner Server
  • Running process on RapidMiner Server
  • Schedule processes
  • Handling error
  • Work with RapidMiner Radoop
  • Text mining with RapidMiner
  • Web mining with RapidMiner
  • Time series analysis
  • Deep learning with RapidMiner

 

Audience & Pre-requisite

This course is designed for data engineer, data analyst, and analytics team. Basic knowledge of computing, including familiarity with SQL and general database concepts.

 

Training Methodology

Lecture, group discussion

Durations

5 (five) days of in-person training / classroom

 

Course Outline

  1. Overview
  2. Advanced Analytics with RapidMiner
  3. Data Preparation
  4. Modelling and Validation
  5. ETL Revised
  6. Beyond Decision Tree Advanced Modelling
  7. Server
  8. Connecting RapidMiner to Hadoop
  9. Exploring Hadoop Cluster
  10. More Settings
  11. Pushing and Retrieving Data form Hadoop
  12. In-Hadoop ETL
  13. Transparent Data Transfer
  14. Feature Set Reduction 1 : Selection
  15. In-Cluster Modelling
  16. Single Process Pushdown
  17. Server
  18. Introduction to Text Mining Concepts
  19. Document Handling in RapidMiner
  20. Text Data Preprocessing
  21. Text Processing
  22. Text Visualization
  23. Predicting Review Ratings with k-NN
  24. Text Clustering
  25. Web Mining – Loading Data
  26. Web Mining – Processing Data
  27. Sentiment Mining with Web Data
  28. Introduction to Time Series Analysis
  29. Manipulating Time Series Data
  30. Correlation Anlysis and The Autocorrelation Function
  31. Autoregression
  32. Simple Moving Average
  33. ARIMA Model
  34. Introduction to Deep Learning
  35. Multilayer Neural Networks
  36. Back Propagation
  37. Activation Function
  38. Calculating Model Errors
  39. Gradient Descent
  40. How to use Deep Learning in RapidMiner
  41. Mini Project
  42. Presentation