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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.

04h:16m
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.

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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

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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


Loonycorn
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.
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    Ratings and Reviews     4.5/5

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