Machine Learning Project: Building a Recommender System

Are you ready to transition from learning machine learning theory to building functional, real-world applications? This text-based course guides you step-by-step through designing, building, and evaluating a complete recommendation system. You will learn how to process data, extract features, apply collaborative and content-based filtering, and implement modern embedding-based recommendation techniques. What you'll learn: - Understand the foundational concepts of recommendation systems, including collaborative filtering and content-based filtering - Process and clean real-world interaction datasets using modern Pandas workflows - Build and train recommendation models using Scikit-learn and TensorFlow - Generate user and item embeddings to implement modern similarity-based search - Evaluate recommender performance using standard industry metrics - Structure your machine learning code into a clean, reproducible pipeline You will start with core recommendation concepts and data preparation, progress through model development with Scikit-learn and neural networks in TensorFlow, and conclude with modern embedding techniques. This course is designed for aspiring data scientists and developers who have basic Python knowledge and want to build their first major machine learning project. Start reading today to turn your machine learning knowledge into a practical, portfolio-ready project.