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Advanced Analytics with RapidMiner

 14 Hours
This instructor-led, live training in Poland (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting. By the end of this training, participants will be able to:
  • Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
  • Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
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RapidMiner for Machine Learning and Predictive Analytics

 14 Hours
RapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.  In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.  By the end of this training, participants will be able to:
  • Install and configure RapidMiner
  • Prepare and visualize data with RapidMiner
  • Validate machine learning models
  • Mashup data and create predictive models
  • Operationalize predictive analytics within a business process
  • Troubleshoot and optimize RapidMiner
Audience
  • Data scientists
  • Engineers
  • Developers
Format of the Course
  • Part lecture, part discussion, exercises and heavy hands-on practice
Note
  • To request a customized training for this course, please contact us to arrange.
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Provisional Courses

RapidMiner for Machine Learning and Predictive Analytics

2025-06-02 09:00
14 hours
Online
2580 PLN (Online)

Advanced Analytics with RapidMiner

2025-06-16 09:00
14 hours
Online
2580 PLN (Online)

Advanced Analytics with RapidMiner

2025-06-30 09:00
14 hours
Online
2580 PLN (Online)

RapidMiner for Machine Learning and Predictive Analytics

2025-07-14 09:00
14 hours
Online
2580 PLN (Online)

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