Summary
In this chapter, you learned how to train a model and how to evaluate a clustering model. Then, we looked at the concept of semi-supervised learning, and how it's different from unsupervised learning. Our semi-supervised model has been trained, and we can now make predictions based on it.
Since this was the last chapter of our book, we'll summarize what we have accomplished. You have learned the basics of machine learning; we've installed JDK, JRE, and NetBeans. We looked at search algorithms, working on and implementing two of them: one was Dijkstra's algorithm and the other one was a modification of it (the A* algorithm).
You learned about game playing, and we implemented a game playing algorithm using tic-tac-toe. We covered what a rule-based system is, and we implemented a basic rule-based system in Prolog; then, we used that rule-based system in our Java program. We installed Weka and worked with datasets. We converted a CSV file into an ARFF file, and vice versa. Then, we applied...
