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
Ex Tax: ¥450,000
- Series: CS-0002