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Crack the Machine Learning Interview

Learn to design real machine learning systems with the help of several open-ended machine learning problems commonly asked during interviews at big tech companies. You will start by identifying the problem statement, understanding scale and latency requirements and defining metrics. Then you will come up with the architecture, select models, gather data and finally execute and evaluate the models offline and online.

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Learning Objectives

Learn practical ML techniques for building ML systems.

Learn how a feed-based system like Twitter is designed.

Learn how self-driving cars of Google and Tesla work.

Learn how a search ranking system like Google is designed.

Learn how a recommendation system like Netflix is designed.

Learn how the ads prediction system at Facebook works.

Hands-On Programs

Practical ML Techniques/Concepts

A quick introduction to performance and capacity considerations and discusses why they matter when designing a solution to a machine learning problem.

Feed Based System

Learn to design a Twitter feed system that will show the most relevant tweets for a user based on their social graph.

Self-Driving Car: Image Segmentation

Learn to design a self-driving car system focusing on its perception component (semantic image segmentation in particular).

Ad Prediction System

Search Ranking

Learn to design a search relevance system for a search engine.

Recommendation System

Learn to display media (movie/show) recommendations for a Netflix user.

Entity Linking System

Learn to build an entity linking system.

Learn to build a system to show relevant ads to users.

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