tertiarycourses / rapidminertraining

Exercise files for Data Mining Training with RapidMine

Data Mining Training with RapidMiner

by Tertiary Infotech Pte. Ltd

These are the exercise files used for Data Mining Training with RapidMiner course.

The course outline can be found in

https://www.tertiarycourses.com.sg/data-mining-training-rapidminer-studio.html

https://www.tertiarycourses.com.my/data-mining-training-rapidminer-studio-malaysia.html

Day 1

Module 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

Module 2: 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

Module 3: Building Better Processes

• Organizing, Renaming, & Relative Paths
• Sub-Processes
• Building Blocks
• Breakpoints

Module 4: Predictive Modeling Algorithms

• k-Nearest Neighbor
• Naïve Bayes
• Linear Regression
• Decision Trees & Rules
• Support Vector Machines
• Logistic Regression

Day 2

Module 5: 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

Module 7: 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

Module 8: Advanced Predictive Modeling Algorithms

• Outlier Detection
• Random Forests
• Ensemble Modeling
• Neural Networks