Build stronger predictive models by understanding how random forests, neural networks, and advanced machine learning architectures support accurate forecasting and informed decisions. This course is designed for data analysts, machine learning practitioners, and learners interested in predictive AI and demand forecasting.

You'll explore random forest fundamentals, tree ensembles, boosting, model tuning, validation, feature importance, and techniques for reducing overfitting. Learn how neural networks identify patterns in structured and unstructured data through multilayer networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Discover advanced forecasting architectures, including Long Short-Term Memory (LSTM) networks and transformers. Examine backpropagation, regularization, transfer learning, loss functions, and hyperparameter optimization to understand how predictive models learn and improve. You'll also evaluate data requirements, computing resources, explainability, inference speed, deployment latency, and fairness when selecting suitable predictive architectures. By the end of the course, you'll be able to compare forecasting models, assess their strengths and limitations, and evaluate architecture choices for different prediction and demand modeling tasks. What makes this course distinctive is its practical focus on connecting machine learning architectures with real-world forecasting trade-offs.