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Data Mining and Predictive Analytics with RapidMiner: Foundations and Advanced

Who Should Attend?


Duration: 4 Days
Training Date
  • 13 – 16 July 2020 (KL)
  • 19 – 22 August 2020 (Bangkok)
  • 12 – 15 October 2020 (KL)

This course prepares analyst to take the knowledge gained and apply it to their own respective data mining problems, solving them quickly and easily. The lessons learnt will be applicable to areas such as customer analytics, targeted marketing, social media analytics, fraud detection, predictive maintenance, resource management, etc.

  • Perform all common data preparations
  • Build sophisticated predictive models
  • Evaluate model quality with respect to different criteria
  • Deploy analytical predictive models
  • Utilize more complex functionality of RapidMiner Studio
  • Apply more sophisticated analytical approaches

Data Mining & Predictive Analytics with RapidMiner: Foundation

  1. Overview
    • Business Scenario
    • Analytics Taxonomy & Hierarchy
    • CRISP DM
    • Data Mining in the Enterprise
  2. Basic Usage
    • User Interface
    • Creating and Managing RapidMiner Repositories
    • Operators and Processes
    • Storing Data, Processes, and Results Sets
  3. EDA: Exploratory Data Analysis
    • Loading Data
    • Quick Summary Statistics
    • Visualizing Data & Basic Chart
  4. Data Preparation
    • Basic ETL (Extract, Transform, and Load)
    • Data Types and Transformations
    • Handling Missing Values
    • Handling Attribute Roles
    • Normalization and Standardization
    • Filtering Examples and Attributes
  5. Building Better Processes
    • Organizing
    • Renaming
    • Relative Path
    • Sub processes
    • Building Blocks
    • Breakpoints
  6. Predictive Model’s Algorithms
    • K Nearest Neighbour
    • Correlations
    • Naive Bayes
    • Linear Regression
    • Rules
    • Decision Trees
  7. Model Construction and Evaluation
    • Machine Learning Theory: Bias,
      Variance, Overfitting and Underfitting
    • Split and Cross Validation
    • Applying Models
    • Optimization and Parameter Tuning
    • Splitting Data
    • Evaluation Methods & Performance Criteria
  8. Additional Workshops
    • Outlier Detection
    • Random Forests
    • Ensemble Modeling

Data Mining & Predictive Analytics with RapidMiner: Advanced

  1. Overview
    • Business Case
    • Intro Course Review
    • Loading New Data
  2. EDA: Exploratory Data Analysis
    • Multiple Data Sources
    • Joins & Set Theory
    • Understanding New Attributes
  3. Data Preparation
    • 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
  4. Predictive Model’s Algorithms
    • Support Vector Machines
    • K Means Clustering
    • Neural Networks
    • Logistic Regression
  5. Model Construction and Evaluation
    • Advanced Performance Criteria
    • ROC Plots
    • Comparison between Models
    • Sampling
    • Weighting
    • Feature Selection: Forward Selection
    • Feature Selection: Backward
    • Validation of Preprocessing and
      Preprocessing Models
    • Optimization & Logging Results
  6. Additional Workshops
    • Principal Components Analysis
    • Logistic Regression
    • Performance (Cost) Model

Register Now

Drop us your entry if you are interested to join this course.


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