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Data Mining Training with RapidMiner

RapidMiner allows organizations to use predictive analytics in order to gain competitive advantage through optimizing their businesses. RapidMiner provides the advanced analytics needed to increase marketing response rates, reduce customer churn, detect machine failures, plan preventive maintenance, and detect fraud, among others

RapidMiner’s unique visual development paradigm lets you derive benefits from analytics more quickly than with any other tool. Results are displayed in easy-to-understand charts that provide the “predictive intelligence” needed for better decision making. Predictionbased actions in the form of millions of integrated micro-predictions can even automate everyday decision making and deliver direct value with every single triggered action.

This 2 days course will cover the following topics:

  • RapidMiner Studio
  • Data Preparation
  • Predictive Models 
  • Model Evalaution
  • Feature Engineering


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Course Code: CRS-N-0042585

Course Booking

$498.00

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.

Course Details

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

    Who Should Attend

    • Data Scientists
    • Data Analytsts
    • Finance Analysts
    • Marketeers

    Prerequisite

    Basic R is assumed.

    Trainers

    R TrainerDwight Nuwan Fonseka have a degree in Biotechnology (from NUS) ,Advanced diploma in Pharamceutical management (from MDIS) and Masters in Education (from NTU). He have 8 years experience of teaching biology at O and A levels/ IB level in international schools in Singapore and overseas.

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