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Machine Learningintermediate

Collaborative Filtering Recommendation System

The End-to-End Recommender System is a machine learning-based application designed to recommend books to users based on collaborative filtering. The project encompasses a complete MLOps pipeline, including data ingestion, validation, transformation, model training, and a web-based user interface for interaction.

13 lectures

What You Will Learn

Master the end-to-end Machine Learning lifecycle for Recommender Systems.
Learn to build and manage modular ML pipelines (Ingestion, Validation, Transformation, Training).
Understand how to deploy ML models as interactive web applications using Streamlit.
Gain experience in Collaborative Filtering and Nearest Neighbors algorithms.
Implement data processing techniques using Pandas and NumPy.
Learn containerization of ML applications using Docker.

System Architecture

Collaborative Filtering Recommendation System Architecture Diagram

High-level architecture overview of the Collaborative Filtering Recommendation System .

What You'll Build

  • An End-to-End Books Recommender System capable of suggesting books based on user interests or similar books.
  • An automated ML Pipeline that handles data ingestion, validation, preprocessing, and model training
  • A user-friendly Web Application tailored for real-time recommendations and poster fetching.
  • A Dockerized application ready for cloud deployment
Collaborative Filtering Recommendation System
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