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Machine Learning Coding Interview Questions

A curated list of problems to prepare for Machine Learning coding interviews—covering data prep, model building, optimization, and evaluation.
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In Machine Learning coding interviews, you are asked to build a classifier from scratch, optimize feature selection for speed, or diagnose why a pipeline breaks during deployment. These challenges reflect the actual coding tasks ML engineers and applied scientists face. You'll work through problems that expose data leakage, test your ability to scale training jobs, or require you to build a complete preprocessing pipeline on noisy input. This isn’t just model accuracy—it’s model durability, clarity of logic, and readiness for production. Whether you're aiming for roles in research-driven teams or engineering-heavy ML platforms, this prep helps you write code that works and explain decisions that hold up in Machine Learning coding interviews.
In Machine Learning coding interviews, you are asked to build a classifier from scratch, optimize feature selection for speed, o...Show More

WHAT YOU'LL LEARN

Coding ML algorithms from the ground up—no libraries, just logic.
Transforming raw data into usable features, while handling edge cases like imbalance or leakage.
Designing pipelines that are modular, testable, and efficient.
Debugging issues like exploding gradients, overfitting, and training crashes.
Explaining the why behind your code: trade-offs in interpretability, runtime, and accuracy.
Coding ML algorithms from the ground up—no libraries, just logic.

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Developed by MAANG Engineers
Every Educative lesson is designed by a team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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