Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Weka is written in Java, which means it can run on any computer that has a Java Virtual Machine.

Here’s a brief overview of how you might use Weka in a machine learning project:

1. Load Data: You can load data from various file formats like CSV, ARFF, or databases through JDBC.
2. Preprocess Data: Weka provides various tools for filtering and transforming your data to get it into the right form for training a model.
3. Choose an Algorithm: You can select from a wide range of algorithms, such as decision trees, neural networks, support vector machines, and more.
4. Train the Model: With the prepared data and chosen algorithm, you can train your model. This will include tuning any hyperparameters that control how the algorithm learns from the data.
5. Evaluate the Model: Weka provides various tools for assessing how well your model is likely to perform on unseen data. This can include cross-validation, confusion matrices, and other statistical measures.
6. Make Predictions: Once you’re satisfied with your model, you can use it to make predictions on new data.
7. Visualize the Results: Various visualization tools are provided to help you understand your data and the behavior of your model.

Weka also provides a graphical user interface that allows you to experiment with different algorithms without writing any code. It’s suitable for both beginners who want a user-friendly interface and experts who may want to implement their algorithms.