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

The ranking model takes the top candidates from multiple sources of candidate generation that we have [discussed](https://www.educative.io/courses/grokking-the-machine-learning-interview/candidate-generation). Then, an ensemble of all of these candidates is created, and the candidates are ranked with respect to the chance of the user watching that video content.

Here, your goal is to rank the content based on the probability of a user watching a media given a user and a candidate media, i.e., P(watch|(User, Media)). 

There are a few ways in which you can try to predict the probability of *watch*. It would make sense to first try a simplistic approach to see how far you can go and then apply complex modelling approaches to further optimize the system.

First, we will discuss some approaches using logistic regression or tree ensemble methods and then a deep learning model with dense and sparse features.

Deep learning should be able to learn through sparse features and outperform simplistic approach. Still, as we discussed in earlier problems, generalization with a deep NN model needs an order of magnitude more data (order of 100 of millions of training examples) and training capacity/time (100x more CPU cycles).

Let's look at modeling options for the 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

Ranking