In this series of liveProjects, you’ll build recommendation systems to help suggest products to the customers of an online store. You’ll create a product rating matrix to help understand user preferences and tastes, then utilize two different libraries—Surprise and Fast.ai—to make product recommendations. You’ll learn each library’s different approach to building recommendation systems and go hands-on with different techniques for building your models.
In this liveProject, you’ll create a product recommendation engine for an online store using collaborative filtering techniques from the Surprise library. You’ll work with Amazon review datasets to create your data corpus, and identify which would be best for a collaborative filtering recommender. You’ll then use two different approaches—neighbourhood-based and matrix factorization—to implement different solutions to the rating matrix completion problem. You’ll learn how to select and clean the necessary data for these different approaches. When you’re finished, you’ll have built a system that can predict the rating for a product a user has not yet purchased.
In this liveProject, you’ll create a recommendation engine for an online store using the Fast.ai library. You’ll utilize a dot product and a neural network to come up with the latent factors in a rating matrix, and compare and contrast them to determine which is likely to deliver the best recommendations. You’ll need to select and clean your data, pick the right methods, then create the functions that you need to recommend products based on predicted ratings.
This liveProject is for beginner Python data scientists interested in creating recommendation engines. To begin this liveProject, you will need to be familiar with the following:TOOLS
In this liveProject, you’ll learn how to put collaborative filtering techniques into action to create recommendation engines, one of the most useful types of machine learning applications. You’ll become familiar with the workflow of a professional data scientist.
geekle is based on a wordle clone.