AI Web Apps: Build a Titanic Survival Predictor with PyTorch and Next.js

Ever wondered how to bridge the gap between machine learning models and real-world web applications? By combining the power of PyTorch with the modern web capabilities of Next.js, you can bring predictive AI directly to users. This text-based course guides you through building a complete, interactive web service that uses the classic Titanic dataset to predict survival probability based on custom passenger data. In this course, you will learn to: - Understand the fundamentals of binary classification and neural network architecture in PyTorch. - Prepare and preprocess tabular data using modern data pipelines. - Train and evaluate a deep learning model to predict survival probabilities. - Build an interactive web interface using Next.js and modern React components. - Create secure API routes to connect your PyTorch model to your web frontend. - Apply best practices for deploying and serving lightweight AI models on the web. The course begins with foundational concepts of machine learning and web integration, guiding you step-by-step through data preparation, model training, and API development. You will work through clear written explanations, code snippets, and structured exercises to build your full-stack AI application. This course is designed for aspiring AI engineers, web developers, and beginners curious about combining machine learning with web technologies. No prior deep learning experience is required, though basic familiarity with Python and JavaScript is helpful. Start building your first interactive AI web service today.