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Set up environment

with Adam Geitgey

This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.

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Welcome

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What you should know before watching this course

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Using the exercise files

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Set up environment

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What is a recommendation system?

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What can you do with recommendation systems?

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Cool uses of recommendation systems

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Content-based recommendations: Recommending based on product attributes

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Collaborative filtering: Recommending based on similar users

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Introduction to NumPy, SciPy, and pandas

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Think in vectors: How to work with large data sets efficiently

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Explore our product recommendation data set

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Represent product reviews as a matrix

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Recommend by predicting missing user ratings

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A simple way to predict missing user ratings

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Latent representations of users and products

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Code the recommendation system

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How matrix factorization works

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Use latent representations to find similar products

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Explore our system’s recommendations

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Use regularization

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Measure recommendation accuracy

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Make recommendations for existing users

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How to handle first-time users

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Find similar products

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Wrap up