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

# Introduction

Recommendation systems are used by most of the platforms we use daily. 

For example:
1. The Amazon homepage recommends personalized products that we might be interested in.
2. The Pinterest feed is full of pins that we might like based on trends and our historical browsing.
3. Netflix shows movie recommendations based on our taste, trending movies, etc.

In this chapter, we will go over the Netflix movie recommendation problem, but a similar technique can be applied to nearly all other recommendation systems.

# Problem statement

The interviewer has asked you to **display media (movie/show) recommendations for a Netflix user.** Your task is to make recommendations in such a manner that the chance of the user watching them is maximized. 

Let's get acquainted with the task of building a recommendation system. 

Problem Statement

Let's get acquainted with the task of building a recommendation 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

Introduction

Problem statement