Introduction to Java ML

Tablesaw

Filtering, summarizing, and visualizing datasets
Looping structures for data iteration
Creating and using custom classes to model data
Managing collections with arrays and ArrayLists

Data Structures in Action

Learn Java fundamentals through data manipulation. Tablesaw makes it easy to apply loops, classes, and arrays in real-world datasets—bridging AP CSA topics with practical data science.

Weka

Selecting appropriate primitive types for ML datasets
Managing data precision and memory using numeric types
Understanding casting and type conversion in feature engineering
Utilizing boolean logic for conditional operations in ML workflows

Primitive Types for ML Data

Master Java’s primitive types with Weka. Explore how data types impact memory, accuracy, and logic in machine learning workflows, while working with Weka’s built-in tools and formats.

Smile

Distinguishing between instance methods and static utilities
Handling nulls and preventing NullPointerExceptions in pipelines
Structuring ML applications with object-oriented design
Using constructors to configure algorithms and datasets

OOP Meets Machine Learning

Apply object-oriented programming in machine learning with Smile. Build and use model objects and manage state with constructors

Practice

Load and explore wine quality datasets
Create visualizations to understand data patterns
Build and compare machine learning models
Make predictions on wine quality

Wine Quality Analysis

Apply what you've learned in Tablesaw, Weka, and Smile to analyze wine quality data and build machine learning models!