Develop a Movie Recommendation Engine

Overview

This course empowers learners to design, develop, and evaluate movie recommendation systems using real-world data and Python programming. Tailored for data enthusiasts and aspiring machine learning developers, the course introduces the practical applications of recommender systems across modern digital platforms such as Netflix, Amazon, and YouTube. Beginning with foundational concepts, learners will set up their Python environment and build a simple recommender based on popularity metrics. As the course progresses, learners will transition to constructing a more nuanced content-based recommender, utilizing rich metadata such as genres, keywords, and cast to provide personalized recommendations. By completing this course, learners will gain hands-on experience in preprocessing data, engineering features, and applying core machine learning techniques for real-time decision-making. The instruction is aligned with Bloom’s Taxonomy, guiding learners to construct, analyze, and apply recommender models effectively.

Syllabus

Building Foundations of a Movie Recommender System

This module introduces learners to the core concepts and implementation of a movie recommendation engine using Python. It begins by contextualizing the significance of recommender systems in modern digital platforms and then guides learners through setting up their development environment, importing essential libraries, and exploring fundamental recommendation techniques. Through a blend of conceptual overview and hands-on coding, learners will develop both a simple popularity-based recommender and a more advanced content-based filtering system using movie metadata.