MACHINE LEARNING

Recommendation Systems

Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

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About the course

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You've seen automated recommendations everywhere - on Netflix's home page, on YouTube, and on Amazon as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them.

Skills you will gain

  • Popularity Based RS
  • Collaborative filtering
  • Content based RS
  • Performance Evaluation

Course Syllabus

Module 1

Recommendation Systems

4.0 Hrs

2 Quizzes
  • What each type of recommendation system offers for the different types of data
  • Key approaches of recommendation system
  • Hands on exercise for popularity based model and item based similarity model
  • Concept of market basket analysis using one particular data
  • Hands on exercise on Market basket analysis
  • Content based recommendation system and its applications
  • Hands on exercise on Content based recommendation system
  • How collaborative filtering works in recommendation systems
  • Matrix Factorization Based approach using SVD and its hands on
  • Performing SVD on different dataset through manual calculations
  • Hands on exercise on collaborating filtering models
  • Hybridization methods
  • Recommendation System Ungraded Project Preview
  • Dataset for Ungraded Project
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Project

Build your own recommendation system

Course certificate

Get Recommendation Systems course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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