Weka Training Courses in Finland

Weka Training Courses

Online or onsite, instructor-led live Weka training courses demonstrate through interactive hands-on practice how to use Weka to perform data mining tasks.

Weka training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Weka training can be carried out locally on customer premises in Finland or in NobleProg corporate training centers in Finland.

Weka is also known as Waikato Environment for Knowledge Analysis.

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Weka (Waikato Environment for Knowledge Analysis) Course Outlines in Finland

Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
This instructor-led, live training in Finland (online or onsite) is aimed at data analysts and data scientists who wish to use Weka to perform data mining tasks.

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

- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.

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Upcoming Waikato Environment for Knowledge Analysis (Weka) Courses in Finland

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Some of our clients

is growing fast!

We are looking for a good mixture of IT and soft skills in Finland!

As a NobleProg Trainer you will be responsible for:

  • delivering training and consultancy Worldwide
  • preparing training materials
  • creating new courses outlines
  • delivering consultancy
  • quality management

At the moment we are focusing on the following areas:

  • Statistic, Forecasting, Big Data Analysis, Data Mining, Evolution Alogrithm, Natural Language Processing, Machine Learning (recommender system, neural networks .etc...)
  • SOA, BPM, BPMN
  • Hibernate/Spring, Scala, Spark, jBPM, Drools
  • R, Python
  • Mobile Development (iOS, Android)
  • LAMP, Drupal, Mediawiki, Symfony, MEAN, jQuery
  • You need to have patience and ability to explain to non-technical people

To apply, please create your trainer-profile by going to the link below:

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