Introduction to Recommendation Systems
- Created By raju2006
- Last Updated December 11th, 2023
- Overview
- Prerequisites
- Audience
- Audience
- Curriculum
Description:
Master Collaborative Filtering in Recommender Systems: Explore Matrix Factorization, SVD, User-Based, and Content-Based Filtering. Build Your Own Recommendation System and Learn Key Evaluation Metrics in this Comprehensive Course.
Course Code/Duration:
BDT171 / 1 Day
Learning Objectives:
After this course, you will be able to:
- Understanding recommendation systems
- Using content-based filtering
- Building model using collaborative filtering
- Using matrix factorization and SVD to build models
- Evaluating a recommendation system
- Understanding of building machine learning models using python.
- This course is designed for anyone interested to get started with building recommendation systems using machine learning and python.
- This course is designed for anyone interested to get started with building recommendation systems using machine learning and python.
Course Outline:
- Understanding recommendations systems
- Types of recommenders
- Understanding different ratings
- Top-N Recommendations
- Content-Based filtering
- Understanding cosine similarity
- Build a content-based recommendation system using Cosine similarity
- Lab: Movie recommendation
- Collaborative filtering
- Understanding User-based collaborative filtering v/s Item-based collaborative filtering
- Building a system using User-based collaborative filtering
- Building a system using Content-based collaborative filtering
- Lab: User-based and Content-based collaborative filtering
- Matrix Factorization and SVD model
- Understanding Principal Component Analysis
- Using Single Value Decomposition (SVD) library to build models
- Lab: Build model using SVD
- Evaluating a recommendation system
- Understanding accuracy metrics
- Top-N hit rate
- Scaling using Apache Spark
- Apache Spark Architecture
- Using ALS, Matrix factorization in Spark
- Lab: Movie recommendation using Spark
Training material provided:
Yes (Digital format)
Hands-on Lab: Virtual machine lab will be provided to every student with appropriate tools installed.
The curriculum is empty
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