Course Outline


Getting Started with Knime

  • What is KNIME?
  • KNIME Analytics
  • KNIME Server

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Preparing the Development Environment

  • Installing and configuring KNIME


  • Adding nodes
  • Accessing and reading data
  • Merging, splitting, and filtering data
  • Grouping and pivoting data
  • Cleaning data


  • Creating workflows
  • Importing data
  • Preparing data
  • Visualizing data
  • Creating a decision tree model
  • Working with regression models
  • Predicting data
  • Comparing and matching data

Learning Techniques

  • Working with random forest techniques
  • Using polynomial regression
  • Assigning classes
  • Evaluating models

Summary and Conclusion


  • Experience with Python
  • R experience


  • Data Scientists
  14 Hours


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