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 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 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.