Machine Learning with Weka

Machine Learning Using Weka

Aim of Course:

Weka is a powerful, open-source machine learning tool. Its users can import data and train many available algorithms to build classification or regression models. This class is a hands-on tutorial that will teach students how to use the Weka platform. We will cover the basics of machine learning including how to choose the right algorithms for your data, and then learn how to format data and import it into Weka, how to build models, and how to analyze and interpret the results.  

In this course, the focus is on learning the Weka tool, in contrast to other courses where the focus is on a more detailed study of the data mining methods.

Anticipated learning outcomes - you will be able to:

  • Navigate Weka, read in data and work with appropriate data formats
  • Use Weka to implement basic data mining algorithms
    • Trees
    • Rule Systems
    • Bayesian Networks
    • Neural Networks
  • Apply appropriate algorithms to classification and regression
  • Assess model results with appropriate metrics
  • Use Weka to classify documents



This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

Week 1: Machine Learning and Weka Basics

  • What is Machine Learning and Weka?
    • Machine learning fundamentals
    • Core algorithm types
    • Trees
    • Rule Systems
    • Bayesian models
    • Neural Networks
    • Weka basics
    • Explorer/experimenter
    • File types
    • Interface elements
  • Classification
    • What is classification? What are classes? 
    • How classification works, high level
    • Classifying data in Weka, including the major Weka features
  • Regression 
    • What is regression? 
    • Which algorithms will work for regression
    • Running regressions in Weka

Week 2: Creating Datasets for Weka

  • Creating ARFF files
    • Formatting data for use in Weka
    • Data types
    • Class enumeration
  • Features and feature types
    • What are features?
    • What are the major feature types?
    • What features work with regression? How do we handle non-numeric features?
    • Filtering algorithms based on feature-type in Weka

Week 3: Interpreting Results

  • Interpreting and Refining Results 
    • Accuracy
    • Precision, recall, F1 scores
    • Confusion Matrices
  • Class Balancing

Week 4: More advanced Weka features

  • Document classification
    • What are word vectors
    • Converting text files to word vectors
  • Saving and Importing modified ARFF files
  • Visual exploration of features
  • Meta-algorithms

Machine Learning with Weka

Who Should Take This Course:
Data scientists who want to get up to speed quickly with the standard data and text mining methods using an open source tool, and who do not want to go the programming route (R or Python).
Familiarity with introductory statistics, including regression
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

 Options for Credit and Recognition:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses
Course Text:
All materials will be provided online.
Weka - open source, free


February 07, 2020 to March 06, 2020 February 12, 2021 to March 12, 2021

Machine Learning with Weka


February 07, 2020 to March 06, 2020 February 12, 2021 to March 12, 2021

Course Fee: $549

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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