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 performance of the user engagement prediction model will depend drastically on the quality and quantity of training data. So let's see how the training data for our model can be generated.




# Training data generation through online user engagement

When we show an ad to the user, they can engage with it or ignore it. 
Positive examples result from users engaging with ads, e.g., clicking or adding an item to their cart. Negative examples result from users ignoring the ads or providing negative feedback on the ad.




Let's learn about the techniques for generating training data for the ad prediction system.

Introduction

Practical ML Techniques/Concepts

Search Ranking

Feed Based System

Recommendation System

Self-Driving Car: Image Segmentation

Entity Linking System

Ad Prediction System

Training Data Generation

Training data generation through online user engagement