Recommendation Systems Theory and Coding in Go | CodeSignal Learn

Skip to main content

beginner

Recommendation Systems Theory and Coding in Go

Artificial Intelligence

Trusted by learners working at top companies

Turn screen time into skills time

Practice anytime, anywhere with our mobile app.

Sign up

Join the 1M+ learners on CodeSignal

Be a part of our community of 1M+ users who develop and demonstrate their skills on CodeSignal

3 courses

58 practices

4 hours

Master the essentials of building recommendation systems from scratch in Go! 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

Tools you'll use

Go

Course 1

Recommendation Systems Foundations with Go

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 and Model-Based Recommendations in Go

5 lessons

22 practices

In this course, learners will dive into content-based recommendation systems, focusing on feature engineering, user and item profiling, and 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

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.

Scan to download

Other paths you may like

Fundamental Coding Interview Prep with Python

Introduction to Programming with Python

Mastering Algorithms and Data Structures in Python

Advanced Coding Interview Preparation with Python

Full-Stack Engineering with JavaScript

Prompt Engineering for Everyone

Journey into Data Science with Python

Java Programming for Beginners

From our community

Hear what our customers have to say about CodeSignal Learn

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

I love that it's personalized. When I'm stuck, I don't have to hope my Google searches come out successful. The AI mentor Cosmo knows exactly what I need.

Faith Yim

Software Engineer

+14

It's an amazing product and exceeded my expectations, helping me prepare for my job interviews. Hands-on learning requires you to actually know what you are doing.

Alex Bush

Full Stack Engineer

+9

I'm really impressed by the AI tutor Cosmo's feedback about my code. It's honestly kind of insane to me that it's so targeted and specific.

Abbey Helterbran

Tech consultant

+8

I tried Leetcode but it was too disorganized. CodeSignal covers all the topics I'm interested in and is way more structured.

Jonathan Miller

Senior Machine Learning Engineer

+12

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

name

Francisco Aguilar Meléndez

Data Scientist

+11

Company

About Careers Leadership Talent Science Newsroom

Collections

Generative AI Business & Leadership Interview Prep AI & Machine Learning Learn to Code Data Science & Engineering

Platform

Platform Overview Skills Assessments Live Tech Interviews AI Interviewer AI Role-Play AI Tutoring with Cosmo

Roles

Talent Acquisition Engineering Leaders Sales Leaders CS & Support Leaders IO Psychologists Individuals

Resources

Resource Library Blog Customer Stories Interview Prep API Docs

Support

Copyright © 2026 CodeSignal

Privacy Terms Compliance & Trust