Minimum Viable Study Plan for Machine Learning Interviews
A comprehensive study plan and resource collection for preparing for machine learning engineering interviews at top tech companies.
What is Minimum Viable Study Plan for Machine Learning Interviews?
khangich/machine-learning-interview is a structured study guide and curated resource repository designed specifically for machine learning engineering interview preparation. It provides a minimum viable curriculum based on real questions from FAANG and other leading tech companies, covering essential topics from coding and statistics to machine learning system design and deep learning fundamentals. The guide distills years of industry experience into an efficient study roadmap to help candidates ace their interviews.
This repository is targeted at machine learning engineers, data scientists, and software engineers preparing for machine learning-focused interviews at top tech companies like Google, Facebook, LinkedIn, and Snapchat. It is especially useful for those seeking a practical, focused approach to interview preparation with real-world questions and structured guidance.
Developers choose this over alternatives because it offers a curated, real-world-focused study plan derived from actual interview experiences at leading companies, including specific machine learning system design use cases like YouTube recommendations and ad click prediction. It provides a comprehensive yet efficient roadmap with practical resources, community testimonials, and links to key papers and implementations, saving time by focusing on high-impact areas.
Overview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
Use Cases
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