# Introduction

<br />

> [!NOTE]
> **Estimated course time:** 4 hours

Welcome to **Recommendation Systems**! We've designed this course
to expand your knowledge of recommendation systems and explain
different models used in recommendation, including matrix
factorization and deep neural networks.

> [!NOTE]
> **Objectives:**
>
> - Describe the purpose of recommendation systems.
> - Understand the components of a recommendation system including candidate generation, scoring, and re-ranking.
> - Use embeddings to represent items and queries.
> - Develop a deeper technical understanding of common techniques used in candidate generation.

## Prerequisites

This course assumes you have:

- Completed [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) either in-person or self-study, or you have equivalent knowledge.
- Familiarity with linear algebra (inner product, matrix-vector product).

*Happy Learning!*