Machine Learning with Java and Weka (Udemy.com)

​Master Java Programming for Machine Learning & Statistical Learning (Weka)

​Data is everywhere, and organizations need engineers who can build intelligent systems to extract meaningful predictions. According to SAS, mastering analytics and machine learning gives you a massive career advantage by sharpening your problem-solving abilities, opening doors to high-demand engineering roles, and unlocking opportunities in cutting-edge fields like the Internet of Things (IoT) and Smart Cities.

​This bite-sized, practical course focuses on Machine Learning and Statistical Learning using Java and the powerful Weka API. It maps directly to the Modeling and Evaluation stages of the industry-standard CRISP-DM framework.

​Why Take This Course?

• ​Hands-On Java ML: Learn how to integrate the Weka library into NetBeans to build, train, and evaluate machine learning models directly within Java applications.

• ​Core Algorithm Coverage: Master key supervised and unsupervised algorithms, including Decision Trees, Naïve Bayes, KNN, Neural Networks, Linear Regression, and Clustering.

• ​CRISP-DM Alignment: Ground your machine learning workflows in real-world data mining lifecycle standards.

• ​Software Development Focus: Go beyond basic scripting by learning how to build a custom Data Mining Java application.

​Recommended Learning Sequence

​To get the most out of this course, follow this learning path:

1. ​Create Your Calculator: Learn Java Programming Basics Fast (Prerequisite)

2. ​Java Programming for Machine Learning and Statistical Learning with Weka (This Course)

​Prerequisite Note: Basic familiarity with Java syntax is recommended. Beginners should start with "Create Your Calculator" first.

​What You Will Learn

​Data Mining Process & Java Project Setup

• ​Understanding the CRISP-DM Framework (Modeling & Evaluation stages)

• ​Setting up NetBeans and configuring the Weka JAR library

• ​Loading datasets and splitting data into Training and Testing sets

​Regression & Clustering (Unsupervised & Supervised)

• ​Linear Regression: Building and tuning linear regression models using Weka and Java

• ​Clustering Algorithms: Implementing K-Means and Agglomerative Hierarchical Clustering

​Classification & Pattern Recognition

• ​Decision Trees: ID3 algorithm and decision tree classification in Java

• ​K-Nearest Neighbors (KNN): Distance-based classification

• ​Naïve Bayes: Probabilistic classification techniques

• ​Neural Networks: Training Multi-layer Perceptrons in Java

​Model Evaluation & Application Development

• ​Algorithm selection strategies ("What Algorithm to Use?")

• ​Evaluating model performance using cross-validation and test metrics in Java

• ​Project: Building your own end-to-end Data Mining Java application

​Requirements

• ​Basic knowledge of Java programming (variables, loops, objects).

• ​A computer (Windows, Mac, or Linux) with JDK, NetBeans IDE, and the Weka library installed.

​Who This Course Is For

• ​Java developers wanting to enter the Machine Learning and Data Science space.

• ​Students and professionals who want to embed predictive models into Java desktop or server applications.

• ​Anyone looking to master the Modeling and Evaluation stages of the CRISP-DM lifecycle using Weka.