Recommendation Systems Theory and Coding in C++ | CodeSignal Learn

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Recommendation Systems Theory and Coding in C++

Artificial Intelligence

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4 courses

74 practices

5 hours

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

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Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Coding and Data Algorithms

INTERMEDIATE

Feature Engineering

ADVANCED

Machine Learning Model Development

INTERMEDIATE

Model Validation and Selection

Tools you'll use

C++

Course 1

Recommendation Systems Foundations in C++

4 lessons

17 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

5 lessons

22 practices

Dive into content-based recommendation systems, focusing on feature extraction, similarity measures, and factorization machines. You will learn to utilize item features and user profiles to build personalized models. This course provides hands-on C++ examples, progressing from simple similarity methods to advanced factorization techniques for robust, data-driven recommendations.

Course 3

Diving Deep into Collaborative Filtering Techniques with ALS

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

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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