Your proven path to success in Machine Learning Interviews – developed by FAANG engineers. Unlock ML loops at top companies with a System Design approach.

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

# Problem statement

The interviewer has asked you to **design a Twitter feed system that will show the most relevant tweets for a user based on their social graph.**



 
 

First, let's develop an understanding of the problem.

# Visualizing the problem 
User A is connected to other people/businesses on the Twitter platform. They are interested in knowing the activity of their connections through their feed.

In the past, a rather simplistic approach has been followed for this purpose. All the Tweets generated by their followees since user A's last visit were displayed in **reverse chronological order**.

However, this *reverse-chronological order feed display* often resulted in user A missing out on some Tweets that they would have otherwise found very engaging. Let's see how this happens.
 
Twitter experiences a large number of daily active users, and as a result, the amount of data generated on Twitter is torrential. Therefore, a potentially engaging Tweet may have gotten pushed further down in the feed because a lot of other Tweets were posted after it.

Hence, to provide a more engaging user experience, it is crucial to rank the most relevant Tweets above the other ones based on user interests and social connections.

Let's look at a problem statement that asks you to design a Twitter feed system.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Problem Statement

Problem statement

Visualizing the problem