Recommendation Engine - Basics

Overview

This hands-on course guides learners through the complete lifecycle of building a movie recommendation system using Python. Beginning with a conceptual overview of recommendation engines and collaborative filtering techniques, learners will identify real-world applications and articulate how these systems drive personalization across platforms. The course progresses through environment setup using Anaconda and dataset preparation, ensuring participants can organize, configure, and manipulate data efficiently. Using the Surprise library, learners will construct machine learning models, validate performance using cross-validation techniques (including RMSE and MAE), and interpret prediction accuracy. Learners will write Python functions to generate personalized movie predictions, gaining practical experience in model evaluation, prediction logic, and iterable handling using tools like islice. By the end of the course, learners will be able to analyze datasets, implement algorithms, and deploy predictive features in a streamlined and reproducible manner. Through interactive coding and progressive exercises, learners will apply, analyze, and create recommendation solutions applicable in real-world data science workflows.

Syllabus

Building a Recommendation Engine from Scratch

This module introduces learners to the foundational concepts and technical workflow of building a recommendation engine using Python. It begins with a conceptual overview of recommendation systems and collaborative filtering, then transitions into preparing the development environment and datasets using Anaconda and the Surprise library. Finally, learners will construct, evaluate, and deploy a predictive model capable of generating personalized movie recommendations using real user data. The focus is on practical application, model evaluation with cross-validation, and generating top predictions through structured Python functions.