• This course is designed for anyone interested to get started with building recommendation systems using machine learning and python.

• Understanding of building machine learning models using python.

Course Outline:

1. Understanding recommendations systems
   1. Types of recommenders
   2. Understanding different ratings
   3. Top-N Recommendations
2. Content-Based filtering
   1. Understanding cosine similarity
   2. Build a content-based recommendation system using Cosine similarity
   3. Lab: Movie recommendation
3. Collaborative filtering
   1. Understanding User-based collaborative filtering v/s Item-based collaborative filtering
   2. Building a system using User-based collaborative filtering
   3. Building a system using Content-based collaborative filtering
   4. Lab: User-based and Content-based collaborative filtering
4. Matrix Factorization and SVD model
   1. Understanding Principal Component Analysis
   2. Using Single Value Decomposition (SVD) library to build models
   3. Lab: Build model using SVD
5. Evaluating a recommendation system
   1. Understanding accuracy metrics
   2. Top-N hit rate
6. Scaling using Apache Spark
   1. Apache Spark Architecture
   2. Using ALS, Matrix factorization in Spark
   3. 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.