Refund Request Filtering with Neural Networks

Automated refund systems are essential for modern digital platforms, but building neural networks to filter fraudulent or invalid requests comes with unique data challenges. Understanding how to define your sample population and prepare data points is critical to training a reliable predictive model. This text-only course guides you through the foundational concepts of neural networks applied to automated decision-making. You will transition from basic theory to structuring real-world classification systems that can accurately filter complex refund requests. What you will learn: Understand the fundamental terminology and architecture of predictive neural networks; Identify and define sample populations and key data points for refund classification; Prepare and preprocess structured data to handle class imbalances and edge cases; Design neural network models to automate binary and multi-class filtering decisions; Evaluate model performance using precision, recall, and modern validation metrics; Implement basic monitoring patterns to keep your predictive system accurate over time. You will start with the core definitions of neural networks and predictive systems before exploring how to select sample populations and engineer features. The course then walks you through model design, training strategies, and performance evaluation using written explanations and practical code examples. This course is designed for beginners in machine learning and data science who want to apply neural networks to practical business automation, with no advanced mathematical prerequisites. Start learning how to build intelligent filtering systems today.