System design is an important component of any ML interview. Being able to efficiently solve open-ended machine learning problems is a key skill that can set you apart from other engineers and increase the level of seniority at which you’re hired.

This course helps you build that skill, and goes over some of the most popularly asked interview problems at big tech companies. You’ll walk step-by-step through solving these problems, focusing in particular on how to design machine learning systems rather than just answering trivia-style questions. 

Once you’re done with the course, you’ll be able to not just ace the machine learning interview at any tech company, but impress them with your ability to think about systems at a high level. If you have a machine learning or system design interview coming up, you’ll find the course tremendously valuable.

Grokking the Machine Learning Interview

The feed-ranking system aims to maximize user engagement. So, let's start by looking at all the user actions on a Twitter feed.

# User actions
The following are some of the actions that the user will perform on their tweet, categorized as positive and negative actions. 

**Positive user actions**

- Time spent viewing the tweet

- Liking a Tweet

- Retweeting

- Commenting on a Tweet  
	
**Negative user actions**

- Hiding a Tweet  

- Reporting Tweets as inappropriate


Now, you need to look at these different forms of engagements on the feed to see if your feed ranking system did a good job. 

> 📝 All of the metrics we are going to discuss can be used to determine the user engagement of the feed generated by our feed ranking system.


Learn about the metrics to capture the success of a feed-ranking system. 

Metrics

Learn about the metrics to capture the success of a feed-ranking system.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Metrics

User actions