Java in Machine Learning

Course Description Master the fundamentals of machine learning using Java in this hands-on course tailored for developers. You’ll use tools like Weka, Smile, and Deeplearning4j to implement ML techniques including regression, classification, and clustering while strengthening your Java skills. In the first module, you’ll get introduced to core machine learning concepts, explore widely-used Java libraries, and understand the full ML workflow from data to model evaluation. The second module focuses on supervised learning. You'll implement regression, logistic regression, and decision trees in Java with step-by-step guidance. In the third module, you’ll dive into unsupervised learning—learning how to use K-Means clustering and apply dimensionality reduction techniques like PCA. The final module brings everything together through end-to-end projects, including data preprocessing, model training, cross-validation, debugging, and deploying your ML models. By the end, you will: -Understand and apply core ML techniques using Java libraries -Apply supervised and unsupervised learning techniques such as regression, classification, and clustering. -Create end-to-end ML workflows in Java, including data preprocessing, model training, and performance evaluation. This course is ideal for: -Java developers who want to transition into machine learning without switching to Python -Software engineers and backend developers looking to add ML capabilities to their Java-based applications -Students or professionals in computer science with basic Java skills who want to explore ML with hands-on implementation -Tech professionals preparing for roles in AI/ML, data science, or intelligent systems where Java is part of the stack" 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.

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.