• Waikato

More Data Mining with Weka

Enhance your skills in practical data mining as you get to grips with using large data sets and advanced data mining techniques.

13,290 enrolled on this course

More Data Mining with Weka
  • Duration

    5 weeks
  • Weekly study

    4 hours

Learn how to process, analyse, and model large data sets

On this course, led by the University of Waikato where Weka originated, you’ll be introduced to advanced data mining techniques and skills.

Following on from their first Data Mining with Weka course, you’ll now be supported to process a dataset with 10 million instances and mine a 250,000-word text dataset.

You’ll analyse a supermarket dataset representing 5000 shopping baskets and learn about filters for preprocessing data, selecting attributes, classification, clustering, association rules, cost-sensitive evaluation.

You’ll also explore learning curves and how to automatically optimize learning parameters.

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Skip to 0 minutes and 6 seconds Hi! I’m Ian Witten from the beautiful University of Waikato in New Zealand, and I’d like to tell you about our new online course More Data Mining with Weka. It’s an advanced version of Data Mining with Weka, and if you liked that, you’ll love the new course. It’s the same format, the same software, the same learning by doing. The aim is the same, as well, to enable you to use advanced techniques of data mining to process your own data and understand what you’re doing.

Skip to 0 minutes and 38 seconds You don’t need to have actually completed the old course in order to embark on the new one, but we won’t be covering things again, so you will need to know something about data mining and the Weka machine learning workbench. The course has short, 5-10 minute video lessons. Slides and captions are available, as well. As before, Weka will be a laboratory for you to learn the practice and the principles of advanced data mining. Each lesson is followed by a carefully designed activity that reinforces what you learned in the lesson. You’re going to do most of your learning actually doing the activities. You won’t learn by listening to me talking or watching me do things, you’ll learn by doing stuff yourself.

Skip to 1 minute and 21 seconds More Data Mining with Weka, coming soon to a computer near you! Hope to see you there!

What topics will you cover?

  • Running large-scale data mining experiments
  • Constructing and executing knowledge flows
  • Processing very large datasets
  • Analyzing collections of textual documents
  • Mining association rules
  • Preprocessing data using a range of filters
  • Automatic methods of attribute selection
  • Clustering data
  • Taking account of different decision costs
  • Producing learning curves
  • Optimizing learning parameters in data mining

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Compare the performance of different mining methods on a wide range of datasets
  • Demonstrate how to set up learning tasks as a knowledge flow
  • Solve data mining problems on huge datasets
  • Apply equal-width and equal-frequency binning for discretizing numeric attributes
  • Identify the advantages of supervised vs unsupervised discretization
  • Evaluate different trade-offs between error rates in 2-class classification
  • Classify documents using various techniques
  • Debate the correspondence between decision trees and decision rules
  • Explain how association rules can be generated and used
  • Discuss techniques for representing, generating, and evaluating clusters
  • Perform attribute selection by wrapping a classifier inside a cross-validation loop
  • Describe different techniques for searching through subsets of attributes
  • Develop effective sets of attributes for text classification problems
  • Explain cost-sensitive evaluation, cost-sensitive classification, and cost-sensitive learning
  • Design and evaluate multi-layer neural networks
  • Assess the volume of training data needed for mining tasks
  • Calculate optimal parameter values for a given learning system

Who is the course for?

This course is aimed at anyone who deals in data professionally or is interested in furthering their professional or academic skills in data science.

This course follows on from Data Mining with Weka and it’s recommended that you complete that course first unless you already have a rudimentary knowledge of Weka.

As with the previous course, it involves no computer programming, although you need some experience with using computers for everyday tasks.

High school maths is more than enough; some elementary statistics concepts (means and variances) are assumed.

What software or tools do you need?

Before the course starts, download the free Weka software. It runs on any computer, under Windows, Linux, or Mac. It has been downloaded millions of times and is being used all around the world.

(Note: Depending on your computer and system version, you may need admin access to install Weka.)

Who will you learn with?

I grew up in Ireland, studied at Cambridge, and taught computer science at the Universities of Essex in England and Calgary in Canada before moving to paradise (aka New Zealand) 25 years ago.

Who developed the course?

The University of Waikato

Sitting among the top 3% of universities world-wide, The University of Waikato prepares students to think critically and to show initiative in their learning.

  • Established

  • Location

    Waikato, New Zealand
  • World ranking

    Top 380Source: QS World University Rankings 2021

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps to help you keep track of your learning
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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