Data Mining with Weka Training Course


Waikato Environment for Knowledge Analysis (Weka) is an open-source data mining visualization software. It provides a collection of machine learning algorithms for data preparation, classification, clustering, and other data mining activities.

This instructor-led, live training (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.

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.


  • Basic knowledge of data mining process and techniques


  • Data Analysts
  • Data Scientists

Course Outline


  • Overview of Weka
  • Understanding the data mining process

Getting Started

  • Installing and configuring Weka
  • Understanding the Weka UI
  • Setting up the environment and project
  • Exploring the Weka workbench
  • Loading and Exploring the dataset

Implementing Regression Models

  • Understanding the different regression models
  • Processing and saving processed data
  • Evaluating a model using cross-validation
  • Serializing and visualizing a decision tree model

Implementing Classification Models

  • Understanding feature selection and data processing
  • Building and evaluating classification models
  • Building and visualizing a decision tree model
  • Encoding text data in numeric form
  • Performing classification on text data

Implementing Clustering Models

  • Understanding K-means clustering
  • Normalizing and visualizing data
  • Performing K-means clustering
  • Performing hierarchical clustering
  • Performing EM clustering

Deploying a Weka Model



Leave a Reply

Your email address will not be published. Required fields are marked *