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Byte-Sized-Chunks: Recommendation Systems
Rating: 3.9 out of 5(141 ratings)
3,232 students

Byte-Sized-Chunks: Recommendation Systems

Build a movie recommendation system in Python - master both theory and practice
Created byLoony Corn
Last updated 3/2016
English

What you'll learn

  • Identify use-cases for recommendation systems
  • Design and Implement recommendation systems in Python
  • Understand the theory underlying this important technique in machine learning

Course content

2 sections • 20 lectures • 4h 16m total length

  • You, This Course, and Us!1:27
  • What do Amazon and Netflix have in common?16:43
    Recommendations - good quality, personalized recommendations - are the holy grail for many online stores. What is the driving force behind this quest?
  • Recommendation Engines - A look inside10:45
    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, collaborative filtering and Association rules are common approaches to do so.
  • What are you made of? - Content-Based Filtering13:35

    Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).

  • With a little help from friends - Collaborative Filtering10:26
    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
  • A Neighbourhood Model for Collaborative Filtering17:51

    Neighbourhood 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.

  • Top Picks for You! - Recommendations with Neighbourhood Models9:41
    We continue with Neighbourhood models and see how to predict the rating of a user for a new product. Use this to find the top picks for a user.
  • Discover the Underlying Truth - Latent Factor Collaborative Filtering20:13

    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.

  • Latent Factor Collaborative Filtering contd.12:09

    Matrix Factorization for Recommendations can be expressed as an optimization problem. Stochastic Gradient Descent or Alternating least squares can then be used to solve that problem.

  • Gray Sheep and Shillings - Challenges with Collaborative Filtering8:12
    Gray Sheep, Synonymy, Data Sparsity, Shilling Attacks etc are a few challenges that people face with Collaborative Filtering.
  • The Apriori Algorithm for Association Rules18:31

    Association rules help you find recommendations for products that might complement the user's choices. The seminal paper on association rules introduced an efficient technique for finding these rules - The Apriori Algorithm

Requirements

  • 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.

Description

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

Who this course is for:

  • Nope! Please don't enroll for this class if you have already enrolled for our 21-hour course 'From 0 to 1: Machine Learning and NLP in Python'
  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! MBA graduates or business professionals who are looking to move to a heavily quantitative role

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