Byte-Sized-Chunks : Recommendation Systems

About Course

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  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.

  • 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

Curriculam

• Section 1: Would You Recommend To A Friend?
  	Lecture 1	You, This Course, and Us!	01:27
  	Lecture 2	What do Amazon and Netflix have in common?	16:43
  	Lecture 3	Recommendation Engines - A look inside	10:45
  	Lecture 4	What are you made of? - Content-Based Filtering	13:35
  	Lecture 5	With a little help from friends - Collaborative Filtering	10:26
  	Lecture 6	A Neighbourhood Model for Collaborative Filtering	17:51
  	Lecture 7	Top Picks for You! - Recommendations with Neighbourhood Models	09:41
  	Lecture 8	Discover the Underlying Truth - Latent Factor Collaborative Filtering	20:13
  	Lecture 9	Latent Factor Collaborative Filtering contd.	12:09
  	Lecture 10	Gray Sheep and Shillings - Challenges with Collaborative Filtering	08:12
  	Lecture 11	The Apriori Algorithm for Association Rules	18:31
  Section 2: Recommendation Systems in Python
  	Lecture 12	Installing Python - Anaconda and Pip	09:00
  	Lecture 13	Back to Basics : Numpy in Python	18:05
  	Lecture 14	Back to Basics : Numpy and Scipy in Python	14:19
  	Lecture 15	Movielens and Pandas	16:45
  	Lecture 16	Code Along - What's my favorite movie? - Data Analysis with Pandas	06:18
  	Lecture 17	Code Along - Movie Recommendation with Nearest Neighbour CF	18:10
  	Lecture 18	Code Along - Top Movie Picks (Nearest Neighbour CF)	06:16
  	Lecture 19	Code Along - Movie Recommendations with Matrix Factorization	17:55
  	Lecture 20	Code Along - Association Rules with the Apriori Algorithm	09:50

What you will get from this course?

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

Who should buy this course?

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  • Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Tech executives and investors who are interested in big data, machine learning or natural language processing
  • MBA graduates or business professionals who are looking to move to a heavily quantitative role
  • 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.