From the course: Building a Recommendation System with Python Machine Learning and AI

Welcome

From the course: Building a Recommendation System with Python Machine Learning and AI

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Welcome

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- [Lillian] Hi, I'm Lillian Pierson, and welcome to the course. We'll be covering the solid essentials of Building Recommendation Systems with Python. In this course, we'll look at all of the different types of recommendation methods there are, and we'll practice building each type of recommendation system. I'll start by introducing you to the core concepts of recommendation systems. Then I'll be showing you how to build a popularity-based recommender by using Python's pandas library. Following that, I'll show you how to recommend similar items based on correlation using pandas. Next, you'll see how to use machine learning classification methods to make a collaborative filtering system by using the logistic regression model from scikit-learn library. After that, I'll show you how to make a model-based collaborative filtering system by using the truncated SVD model also from scikit-learn. Then you'll see how to make a content-based recommender by using the nearest neighbor approach. Lastly, I'll be showing you how to evaluate the completeness and precision of your models by using scikit-learn's classification metrics. So in this course, we'll be covering the popularity based recommender, both types of collaborative filtering systems, and content-based recommenders, plus some other tools and techniques. Now, let's get started.

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