Data Mining with Weka (FutureLearn)

We’ll dispel the mystery that surrounds the subject. We’ll explain the principles of popular algorithms. We’ll show you how to use them in practical applications. You’ll get plenty of experience actually mining data during the course, and afterwards you’ll be well equipped to mine your own. Weka originated at the University of Waikato in NZ, and Ian Witten has authored a leading book on data mining.
This course is part of the Practical Data Mining program, which will enable you to become a data mining expert through three short courses.

What topics will you cover?

• What is data mining?
• Where can it be applied?
• How do simple classification algorithms work?
• What are their strengths and weaknesses?
• In what ways are real-life classification methods more complex?
• How should you evaluate a classifier’s performance?
• What is “overfitting” and how can you combat it?
• How can ensemble techniques combine the result of different algorithms?
• What ethical considerations arise when mining data?

What will you achieve?

• Demonstrate use of Weka for key data mining tasks
• Evaluate the performance of a classifier on new, unseen, instances
• Explain how data miners can unwittingly overestimate the performance of their system
• Identify learning methods that are based on different flavors of simplicity
• Apply many different learning methods to a dataset of your choice
• Interpret the output produced by classification methods
• Describe the principles behind many modern machine learning methods
• Compare the decision boundaries produced by different classification algorithms
• Debate ethical issues raised by mining personal data