Quickly Learn to Make a Recommendation Engine in Python [Course]
Ever wanted to learn to make a recommendation engine in Python? I mean, recommendation engines are a seriously valuable data science product. Just ask NetFlix and Amazon! They’ve made cargo ships of cash off these quirky little contraptions. They’re actually not all that complicated to make either, but there aren’t that many courses out there to show you how. That’s one reason I thought it was important to make my latest LinkedIn Learning course, called Introduction to Python Recommendation Systems.
In this course, you’ll discover how to use Python—and some essential machine learning concepts—to build programs that can make recommendations. In this hands-on course, I cover the different types of recommendation systems out there, and, for each type, I show you how to make a recommendation engine in Python. I also teach you the concepts behind how recommendation systems work by taking you through a series of examples and exercises.
Once you’re familiar with the underlying concepts, I explain how to apply statistical and machine learning methods to construct your own recommenders. You’ll see how to:
- Build a popularity-based recommender using the Pandas library
- How to recommend similar items based on correlation, and
- How to deploy various machine learning algorithms to make recommendations
At the end of the course, you’ll see how to evaluate which recommender performed the best. Don’t spend days hitting your head against a brick wall trying to figure out how to build a recommendation engine in Python! Instead, let me show you how in this short 1.5 hour course.
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