Watch Demo

Rs. 999  Rs. 599

Recommendation Systems Tutorial for Beginners

Created by Stanford and IIT alumni, this Recommender system tutorial teaches collaborative filtering, content-based filtering and movie recommendations in Python enabling you to create your own, personalized, and smart recommendation engines.

Lifetime access
27 learners
Introduction to the Course

Have you ever been impressed by the video recommendations on YouTube or Netflix? This is a result of the work done by their Recommendation Systems. Moreover, the success of an e-commerce website is highly dependent on the efficiency of the Recommendation Engines that they use. The main function of a recommendation engine is to help the user find products that are most relevant to her. Recommendation engines use approaches like content-based filtering, matrix factorization, collaborative filtering, and association rules to make suitable recommendations. This recommendation system tutorial teaches you to do just that.

Read more

Course Objectives

What will you gain from this course?

  • An understanding of the theoretical aspects of recommendation systems in Machine Learning
  • An understanding of neighbourhood models and how to confront the challenges that you will encounter while using them
  • The ability to identify use-cases for recommendation systems
  • The skills required to design and implement recommendation systems in Python

Read more

Prerequisites and Target Audience

Having knowledge of undergraduate-level Mathematics will make it easier for you to understand this course, however, it is not a requirement. If you want to run the source code that is given, you will require working knowledge of Python.

Course Plan
Certificate of completion

1. Introduction
1 video
Introduction to the course 01:28
2. Would you Recommend to a friend?
10 videos
What are you made of? - Content-Based Filtering 13:36

With a little help from friends - Collaborative Filtering 10:27

A Neighbourhood Model for Collaborative Filtering 17:51

Top Picks for You! - Recommendations with Neighbourhood Models 09:42

Latent Factor Collaborative Filtering contd. 12:09

Gray Sheep and Shillings - Challenges with Collaborative Filtering 08:12

The Apriori Algorithm for Association Rules 18:32
3. Recommendation Systems in Python
9 videos
Installing Python - Anaconda and Pip 09:00

Back to Basics : Numpy in Python 18:06

Back to Basics : Numpy and Scipy in Python 14:19

Movielens and Pandas 16:45

Code Along - What's my favorite movie? - Data Analysis with Pandas 06:19

Code Along - Movie Recommendation with Nearest Neighbour CF 18:10

Code Along - Top Movie Picks (Nearest Neighbour CF) 06:16

Code Along - Movie Recommendations with Matrix Factorization 17:56

Code Along - Association Rules with the Apriori Algorithm 09:51

Meet the Author

4 Alumni of Stanford, IIM-A, IITs and Google, Microsoft, Flipkart

Loonycorn is a team of 4 people who graduated from reputed top universities. Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh have spent years (decades, actually) working in the Tech sector across the world.

  • Janani: Graduated from Stanford and has worked for 7 years at Google (New York, Singapore). She also worked at Flipkart and Microsoft.
  • Vitthal: Studied at Stanford; worked at Google (Singapore), Flipkart, Credit Suisse, and INSEAD.
  • Swetha: An IIM Ahmedabad and IIT Madras alumnus having experience of working in Flipkart.
  • Navdeep: An IIT Guwahati alumnus and Longtime Flipkart employee.
  • More from Loonycorn
    Ratings and Reviews     4.5/5

    You may also like