Data Mining with Weka and More Data Mining with Weka are underway!
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Topics covered in Data Mining with Weka:
- 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?
Topics covered in More Data Mining with Weka:
- 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