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Curs

Building Recommendation Engines in Python

IntermediarNivel de competențe

Actualizat 04.2024

Learn to build recommendation engines in Python using machine learning techniques.

Începe cursul gratuit

PythonMachine Learning

4 h

16 videoclipuri

60 Exerciții

4,850 XP

12,780

Certificat de realizare

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Descrierea cursului

We’ve come to expect personalized experiences online—whether it’s Netflix recommending a show or an online retailer suggesting items you might also like to purchase. But how are these suggestions generated? In this course, you’ll learn everything you need to know to create your own recommendation engine. Through hands-on exercises, you’ll get to grips with the two most common systems, collaborative filtering and content-based filtering. Next, you’ll learn how to measure similarities like the Jaccard distance and cosine similarity, and how to evaluate the quality of recommendations on test data using the root mean square error (RMSE). By the end of this course, you’ll have built your very own movie recommendation engine and be able to apply your Python skills to create these systems for any industry.

Cerințe prealabile

Supervised Learning with scikit-learn

1

Introduction to Recommendation Engines

What problems are recommendation engines designed to solve and what data are best suited for them? Discern what insightful recommendations can be made even with limited data, and learn how to create your own recommendations.

What are recommendation engines?

Recommendation engines vs. predictions

Identifying the correct data for recommendation engines

Implicit vs. explicit data

Non-personalized recommendations

Introduction to non-personalized recommendations

Improved non-personalized recommendations

Combining popularity and reviews

Non-personalized suggestions

Finding all pairs of movies

Counting up the pairs

Making your first movie recommendations

Începe capitolul

2

Content-Based Recommendations

Discover how item attributes can be used to make recommendations. Create valuable comparisons between items with both categorical and text data. Generate profiles to recommend new items for users based on their past preferences.

Intro to content-based recommendations

Why use content-based models?

Creating content-based data

Understanding the content-based data

Making content-based recommendations

Comparing individual movies with Jaccard similarity

Comparing all your movies at once

Making recommendations based on movie genres

Text-based similarities

Instantiate the TF-IDF model

Creating the TF-IDF DataFrame

Comparing all your movies with TF-IDF

Making recommendations with TF-IDF

User profile recommendations

Build the user profiles

User profile based recommendations

Începe capitolul

3

Collaborative Filtering

Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.

Collaborative filtering

Pivoting your data

Finding similar users

Challenges with missing values

Compensating for incomplete data

Finding similarities

User-based to item-based

Similar and different movie ratings

Finding similarly liked movies

Using K-nearest neighbors

Stepping through K-nearest neighbors

Getting KNN data in shape

KNN predictions

Item-based or user-based

Comparing item-based and user-based models

Which should you choose?

Începe capitolul

4

Matrix Factorization and Validating Your Predictions

Understand how the sparsity of real-world datasets can impact your recommendations. Leverage the power of matrix factorization to deal with this sparsity. Explore the value of latent features and use them to better understand your data. Finally, put the models you’ve discovered to the test by learning how to validate each of the approaches you’ve learned.

Dealing with sparsity

Matrix sparsity

Limited data in your rows

Matrix multiplication

Matrix factorization

Identifying latent features

Information loss in factorization

Singular value decomposition (SVD)

Normalize your data

Decomposing your matrix

Recalculating the matrix

Making recommendations with SVD

Validating your predictions

Calculating RMSE

Comparing recommendation methods

Începe capitolul

Building Recommendation Engines in Python

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