Data Mining

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?

By the end of the book, you'll be able to...

• 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