Two-Project Series
Recommendation System with Surprise and Fast.ai you own this product
- prerequisites
- basics of Python • basics of pandas • basics of scikit-learn • basics of machine learning • basics of Fast.ai
- skills learned
- build an item recommendation system with collaborative filtering • work with the Surprise and Fast.ai libraries • select, clean and choose the best user rating dataset
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lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
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.
These projects are designed for learning purposes and are not complete, production-ready applications or solutions.
Manning author Ariel Gamino shares what he likes about the Manning liveProject platform.
here's what's included
Project 1 Surprise
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.
Project 2 Fast.ai
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.
book resources
When you start each of the projects in this series, you'll get full access to the following book for 90 days.
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$249.99
only $20.83 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
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Recommendation System with Surprise and Fast.ai project for free
Recommendation System with Surprise and Fast.ai project for free
team
monthly
annual
$49.99
$499.99
only $41.67 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Recommendation System with Surprise and Fast.ai project for free
Prerequisites
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- Basics of Python
- Basics of Pandas and dataframe filtering and manipulation
- Basics of scikit-learn
- Basics of Fast.ai
- Basics of machine learning
- Understanding the concept of train-test split for model performance evaluation
you will learn
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.
- Selecting, cleaning and choosing data for collaborative filtering
- Neighborhood and model based Surprise collaborative filtering techniques
- Learning latent factors for collaborative filtering recommenders with Fast.ai
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- book resources
- Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
