Traverse Trees for ML with BFS & DFS

Data that requires decisions and classifications are everywhere. Decision trees help to create solid data inferences for some of the most common types of machine learning problems. To take advantage of this structure, you need to understand how to properly traverse and build rulesets from decision trees. In this course, you'll learn the fundamentals of decision trees, understanding how to implement the structures in Java. From here, you'll explore some different methods of tree traversals, focusing on BFS and DFS. With BFS and DFS, you'll be able to apply tree traversals to generate tree rulesets. With this knowledge, you'll be equiped to implement and traversal decision trees. This course is for Java developers with a solid programming background, focusing on decision trees, BFS, DFS, and rule generation for machine learning and data classification. A solid understanding of Java programming is crucial for implementing decision trees and traversal algorithms. Additionally, some familiarity with trees as a data structure will help, as decision trees rely on hierarchical structures. By the end of this course, you'll have the skills to confidently implement tree traversal algorithms like BFS and DFS, and generate powerful rules from decision trees to tackle real-world machine learning problems

• Analyze differences between BFS and DFS
• Implement BFS and DFS in Java
• Apply tree traversal algorithms to generate rulesets

• Prior hands-on experience with core concepts
• Ability to independently apply main tools or methods