Overview

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. Disclaimer: This course is an independent educational resource developed by Board Infinity and is not affiliated with, endorsed by, sponsored by, or officially associated with Oracle Corporation or any of its subsidiaries or affiliates. This course is not an official preparation material of Oracle Corporation. All trademarks, service marks, and company names mentioned are the property of their respective owners and are used for identification purposes only.

Syllabus

Data Handling & Preprocessing with Java

Data Handling & Preprocessing with Java focuses on the essential first step of any machine learning pipeline—preparing data for model training. This module introduces learners to key concepts such as data cleaning, normalization, feature selection, and transformation, all within the context of Java-based development. Using libraries like Weka and Tribuo, learners will gain practical experience in managing datasets, handling missing values, encoding categorical variables, and scaling features. The module emphasizes the importance of high-quality input data and walks through end-to-end preprocessing workflows tailored to real-world Java applications. By mastering these techniques, learners will be equipped to build reliable, accurate machine learning models that are grounded in well-structured, meaningful data.

Deep Learning in Java

Deep Learning in Java introduces learners to the fundamentals of deep learning and demonstrates how to build and deploy neural networks using Java-based frameworks. This module begins by explaining key concepts such as artificial neurons, activation functions, backpropagation, and multi-layer architectures. Learners will explore how deep learning differs from traditional machine learning, and where it excels—especially in tasks involving images, text, and complex data patterns. The hands-on portion of the module focuses on building and training deep learning models using libraries like DeepLearning4J (DL4J), covering tasks such as image classification and sentiment analysis. Learners will also learn how to fine-tune models, manage training processes, and evaluate model performance. By the end of this module, learners will have the confidence to apply deep learning in real-world Java applications.

Specialized Libraries & Techniques

Specialized Libraries & Techniques explores advanced tools and strategies that extend the capabilities of machine learning in Java. This module introduces learners to a variety of specialized Java libraries designed for specific tasks such as natural language processing (NLP), time series forecasting, and reinforcement learning. Learners will gain hands-on experience with tools like ND4J for numerical computing, Smile for statistical learning, and Stanford CoreNLP for text analysis. In addition to tool-based learning, this module covers advanced ML techniques such as hyperparameter tuning, ensemble modeling, and model serialization. The focus is on equipping learners with a broader toolkit and deeper insight into solving complex problems efficiently and effectively within Java environments.