Data Mining with RapidMiner

Data Mining with RapidMiner

SkillsFuture Credit Approved Course

Introduction

RapidMiner allows you to use predictive analytics in order to gain an advantage by optimizing your business. Use RapidMiner’s advanced analytics to increase marketing response rates, reduce customer churn and more.

RapidMiner’s easy-to-understand charts provides on-the-spot intelligence for better decision making. Furthermore, learn about prediction-based actions to automate everyday decision making and deliver value with every single triggered action.

 

Course Content

  1. Getting Started with RapidMiner Studio
  • User Interface
  • Creating and Managing RapidMiner Repositories
  • Operators and Processes
  • Storing Data, Processes, and Result Sets
  • Loading Data
  • Visualizing Data & Basic Charting
  1. Data Preparation
  • Basic Data ETL (Extract, Transform, and Load)
  • Data Types & Transformations of Value Types
  • Handling Missing Values
  • Handling Attribute Roles
  • Filtering Examples and Attributes
  • Normalization and Standardization
  1. Building Better Processes
  • Organizing, Renaming, & Relative Paths
  • Sub-Processes
  • Building Blocks
  • Breakpoints
  1. Predictive Modelling Algorithms
  • k-Nearest Neighbour
  • Naïve Bayes
  • Linear Regression
  • Decision Trees & Rules
  • Support Vector Machines
  • Logistic Regression
  1. Model Construction and Evaluation
  • Machine Learning Theory: Bias, Variance, Overfitting & Underfitting
  • Splitting Data
  • Split and Cross Validation
  • Evaluation Methods & Performance Criteria
  • Optimization and Parameter Tuning
  • Applying Models
  • ROC Plots
  • Comparison between Models
  • Sampling
  • Weighting
  • Feature Selection: Forward Selection
  • Feature Selection: Backward Elimination
  • Dimensionality Reduction: Principal Components Analysis (PCA)
  • Validation of Preprocessing and Preprocessing Models
  • Optimization & Logging Results
  1. Advanced Data Preparation
  • Multiple Sources
  • Joins & Set Theory
  • Understanding New Attributes
  • Advanced Data ETL (Extract, Transform, and Load)
  • Aggregation & Multi-Level Aggregation
  • Pivot & De-Pivot
  • Calculated Values
  • Regular Expressions
  • Changing Value Types
  • Feature Generation and Feature Engineering
  • Loops
  • Macros
  1. Advanced Predictive Modelling Algorithms
  • Outlier Detection
  • Random Forests
  • Ensemble Modelling
  • Neural Networks