Machine Learning with Weka
The best way to get started in machine learning is by practicing on real problems.
Weka is the perfect platform for beginning and practicing machine learning because it’s free, has a GUI and provides state-of-the-art algorithms.
Once you know the process of practical machine learning, you can apply it to project after project.
Weka is a collection of machine learning algorithms for data mining tasks.
The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
It is also well-suited for developing new machine learning schemes.
The name Weka project is adopted from a flightless bird (with an inquisitive nature) that is found only on the islands of New Zealand. Project Weka stands for Waikato Environment for Knowledge Analysis.
Applied Machine Learning Process
- Problem Definition: Understand and clearly describe the problem that is being solved.
- Analyze Data: Understand the information available that will be used to develop a model.
- Prepare Data: Discover and expose the structure in the dataset.
- Evaluate Algorithms: Develop a test harness and baseline accuracy from which to improve.
- Improve Results: Leverage results to develop more accurate models.
- Present Results: Describe the problem and solution so that it can be understood by third parties.