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Byte-Sized-Chunks : Recommendation Systems

Build a movie recommendation system in Python - master both theory and practice
Byte-Sized-Chunks : Recommendation Systems
  • 1299
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: Online Course
: Loonycorn
Displaying 1-4 of 4 result(s).

About Course

  •  

    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

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.
 
Discussion
 
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Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.

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