Machine Learning Fundamentals for Java Developers

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