Course Description

Master end-to-end machine learning implementation using Java and its powerful ecosystem. This hands-on course helps you build ML models using tools like Tribuo, Weka, and DeepLearning4j, while also showing how to scale and deploy models using Spark, Mahout, PMML, and ONNX. No prior ML background required—just Java fundamentals and a drive to build real-world intelligent systems. In the first module, you’ll learn how to load, clean, and preprocess datasets using Weka and Tribuo, then build your first regression and classification models from scratch. The second module focuses on deep learning. You’ll use DeepLearning4j to develop neural networks and build an image classifier for the MNIST dataset. In the final module, you'll explore Natural Language Processing with OpenNLP, scale machine learning pipelines with Spark and Mahout, and learn how to export models using formats like PMML and ONNX for real-world deployment. By the end, you will: -Apply data preprocessing techniques using Java tools like Weka and Tribuo for machine learning tasks. -Build, train, and evaluate classification, regression, and deep learning models using DL4J, Tribuo, and DJL. -Implement NLP and scalable machine learning workflows using Apache OpenNLP, Spark MLlib, and Mahout. -Build NLP pipelines, scale to big data, and deploy using PMML/ONNX Target Audience: This course is ideal for: -Java developers who want to build practical machine learning and deep learning solutions. -Backend engineers seeking to integrate scalable ML into Java-based systems. -Data engineers looking to explore ML deployment and model interoperability using Java. -ML enthusiasts who prefer working in the Java ecosystem rather than switching to Python.