Recommendation Systems Theory and Coding in JS | CodeSignal Learn

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Recommendation Systems Theory and Coding in JS

Artificial Intelligence

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4 courses

73 practices

15 hours

Master the essentials of building recommendation systems from scratch in JavaScript! This course covers collaborative filtering, content-based methods, hybrid techniques, and evaluation metrics through hands-on projects and real-world applications.

Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Coding and Data Algorithms

ADVANCED

Machine Learning Model Development

INTERMEDIATE

Model Validation and Selection

Tools you'll use

JavaScript

NodeJS

Course 1

Recommendation Systems Foundations using JS

4 lessons

16 practices

This course introduces foundational algorithms and concepts that form the backbone of recommendation systems. You'll start with simple baseline prediction models and gradually advance to similarity measures and more sophisticated prediction models. Mastering these fundamentals is essential for developing robust and efficient recommendation tools.

Course 2

Content-Based Recommendation Systems using JS

5 lessons

22 practices

In this course, learners will dive into content-based recommendation systems, focusing on factorization machines. These approaches utilize item features and user profiles to make recommendations. The course provides hands-on coding examples to demonstrate how to develop content-based models that harness rich data for personalized recommendations.

Course 3

Collaborative Filtering Techniques with ALS using JS

5 lessons

19 practices

This course explores collaborative filtering techniques, which are central to modern recommendation systems. It covers both user-based and item-based collaborative filtering methods, as well as matrix factorization and the powerful Alternating Least Squares algorithm.

Course 4

Recommendation Systems Quality Evaluation using JS

4 lessons

16 practices

This course focuses on metrics specific to recommendation systems, crucial for evaluating and optimizing model performance. You'll delve into recommendation-specific metrics such as Coverage, Serendipity, Novelty, and Diversity. Each metric is presented with theoretical insights and practical coding examples to illustrate their application.

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