KNIME with Python and R for Machine Learning Treningskurs




14 timer (vanligvis 2 dag inkludert pauser)


  • Experience with Python
  • R experience


  • Data Scientists


KNIME is an open source data analytical software for integrating machine learning and data mining through data pipelines. With Python and R, users are able to extend KNIME in its capabilities for data analytics and machine learning.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

  • Plan, build, and deploy machine learning models in KNIME.
  • Implement end to end data science projects.
  • Make data driven decisions for operations.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.




NobleProg and KNIME design, build and deliver end-to-end advanced analytics solutions that are customized to each customer's business needs.

By combining KNIME's leading open solution for data driven innovation with NobleProg's domain and technical expertise in analytics, we help our customers reduce costs and gain data-driven insights for better business outcomes.



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



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