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20 Videos (4h 16m)
    • You, This Course, and Us!

      1:27
    • What do Amazon and Netflix have in common?

      16:43
    • Recommendation Engines - A look inside

      10:45
    • What are you made of? - Content-Based Filtering

      13:35
    • With a little help from friends - Collaborative Filtering

      10:26
    • A Neighbourhood Model for Collaborative Filtering

      17:50
    • Top Picks for You! - Recommendations with Neighbourhood Models

      9:41
    • Discover the Underlying Truth - Latent Factor Collaborative Filtering

      20:13
    • Latent Factor Collaborative Filtering contd.

      12:09
    • Gray Sheep and Shillings - Challenges with Collaborative Filtering

      8:12
    • The Apriori Algorithm for Association Rules

      18:31
    • Installing Python - Anaconda and Pip

      9:00
    • Back to Basics : Numpy in Python

      18:05
    • 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

      6:18
    • Code Along - Movie Recommendation with Nearest Neighbour CF

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

      6:16
    • Code Along - Movie Recommendations with Matrix Factorization

      17:55
    • Code Along - Association Rules with the Apriori Algorithm

      9:50

About This Class

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Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

  • Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user.
  • Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).
  • Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to Recommendations
  • Neighborhood models - also known as Memory based approaches - rely on finding users similar to the active user. Similarity can be measured in many ways - Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.
  • Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.
  • Recommendation Systems in Python!
  • Movielens is a famous dataset with movie ratings.
  • Use Pandas to read and play around with the data.
  • Also learn how to use Scipy and Numpy

 

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Janani Ravi Vitthal Srinivasan

An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore, and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching ...

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Technology Data Science Machine Learning