In order to prepare for a machine learning interview, developers should focus on key topics like algorithms, data preprocessing, model evaluation, and common frameworks. The next step follows: practicing coding problems, reviewing machine learning concepts, and building projects.
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Grokking the Machine Learning Interview
Your proven path to success in Machine Learning Interviews, developed by FAANG engineers. Unlock ML loops at top companies with a System Design approach.
4.6
64 Lessons
6 Mock Interviews
15h
Updated this week
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Solve open-ended ML System Design problems using a structured, step-by-step methodology.
- Design 6 real-world systems: search ranking, feed optimization, recommendations, ad prediction, and more.
- Select appropriate metrics, model architectures, and training strategies for each system type.
- Apply practical ML techniques, including embeddings, transfer learning, and model debugging.
- Practice with 5 mock interviews simulating real FAANG ML loops.
Why choose this course?
Unlock the ML interview
ML drives the new industrial age. Mastering scalable prediction engines, generative AI, and MLOps makes you indispensable. High-impact roles await those who can engineer intelligent systems and ace the machine learning interview.
Learn to design 6 real-world systems
Crack ML interviews with a system-level approach. Master architectural components, metrics, and modeling strategies through six real-world problems. From search ranking to ad prediction, learn to solve open-ended ML challenges methodically.
Playbook developed by ex-MAANG engineers
Master ML interviews with insider expertise from Big Tech pros. Get AI Mock Interviews for instant feedback and direct access to developer advocates. Learn from Meta, Google, and Microsoft experts to master every ML concept.
Learning Roadmap
1.
Introduction
Introduction
Get familiar with the essentials of ML interviews and key steps in designing ML systems.
2.
Practical ML Techniques/Concepts
Practical ML Techniques/Concepts
Walk through practical ML strategies, covering performance, data collection, experimentation, embeddings, transfer learning, and model debugging.
3.
Search Ranking
Search Ranking
8 Lessons
8 Lessons
Work your way through designing search ranking systems, selecting metrics, and filtering results effectively.
4.
Feed Based System
Feed Based System
9 Lessons
9 Lessons
Build a foundation in designing and optimizing a Twitter feed system for user engagement.
5.
Recommendation System
Recommendation System
7 Lessons
7 Lessons
Generate personalized recommendations by leveraging data on user interactions, watch history, and preferences.
6.
Self-Driving Car: Image Segmentation
Self-Driving Car: Image Segmentation
5 Lessons
5 Lessons
See how it works to enhance self-driving cars with advanced image segmentation techniques.
7.
Entity Linking System
Entity Linking System
5 Lessons
5 Lessons
Build on named entity linking (NEL) with recognition, disambiguation, metrics, architecture, and modeling insights.
8.
Ad Prediction System
Ad Prediction System
7 Lessons
7 Lessons
Learn how to use machine learning to optimize ad relevance and user engagement.
9.
Fraud Detection System
Fraud Detection System
5 Lessons
5 Lessons
Identify unusual or suspicious transactions in financial data to prevent fraudulent activity.
10.
Hate Speech Detection
Hate Speech Detection
5 Lessons
5 Lessons
Classify text content to detect and flag offensive or abusive language online.
11.
Dynamic Pricing Engine
Dynamic Pricing Engine
5 Lessons
5 Lessons
Adjust product prices in real time based on user context, market conditions, and observed demand.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
Developed by MAANG Engineers
ABOUT THIS COURSE
Machine learning interviews at top tech companies now focus more on open-ended system design problems. “Design a recommendation system.” “Design a search ranking system.” “Design an ad prediction pipeline.” These questions evaluate your ability to reason about machine learning systems end-to-end. However, most candidates prepare for isolated concepts instead of system-level design.
This course focuses specifically on building that System Design muscle. You’ll work through 6 real-world ML System Design problems (the same questions asked at Meta, Google, Amazon, and Microsoft) and learn a repeatable methodology for breaking each one down: defining the problem, choosing metrics, selecting model architectures, designing data pipelines, and evaluating trade-offs.
Each system you design builds on practical ML techniques covered earlier in the course: embeddings, transfer learning, online experimentation, model debugging, and performance considerations. By the time you’re designing your third or fourth system, you'll have the technical vocabulary and judgment to explain why your design choices work. This is exactly what interviewers are looking for.
The course also includes 5 mock interviews so you can practice articulating your designs under realistic conditions. If you have an ML or System Design interview coming up at any major tech company, this course will help you walk in with a clear framework for tackling whatever they throw at you.
ABOUT THE AUTHOR
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
Trusted by 2.9 million developers working at companies
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Kang Jun Ong
Software Engineer @ CoffeeSpace
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Sanjay C
Learner
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Ethan Koch
A
Anthony Walker
@_webarchitect_
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Souvik Kundu
Front-end Developer
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Vinay Krishnaiah
Software Developer
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Eric Downs
Musician/Entrepreneur
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