Spaces:
Running
title: ML Interview Prep
emoji: 🎯
colorFrom: yellow
colorTo: red
sdk: static
app_file: index.html
pinned: false
license: mit
short_description: Practice ML and Data Science interview questions
ML Interview Prep
A static, self-contained page for drilling machine learning and data science interview questions. 15 hand-written questions across 7 categories, each with a thorough answer and company/topic tags.
Answers ship redacted and stay covered until you ask for them, because reading an answer you recognise is not the same as producing one under pressure.
Everything runs in the browser. There is no server, no model call, and nothing you type leaves the page -- which also means it never sleeps and never queues.
Two modes
Drill -- a random question from your filtered pool. The answer sits behind redaction bars until you press Reveal. Questions do not repeat until the pool is exhausted.
Browse -- the full set, with substring search over questions and answers, and answers collapsed so you can scan.
Filters
Category, difficulty, and asked at (company) -- all multi-select, and they combine. Picking nothing in a group means that group is not filtering.
| Group | Values |
|---|---|
| Category | Statistics, ML Theory, Deep Learning, NLP, System Design, Feature Engineering, A/B Testing |
| Difficulty | easy, medium, hard (shown as ordinal marks, since difficulty is ordered) |
| Asked at | 15 companies, from Google and Meta to Anthropic and Stripe |
Example questions
Statistics (easy)
Explain the difference between Type I and Type II errors.
ML Theory (medium)
Explain the bias-variance tradeoff.
System Design (hard)
Design a recommendation system for an e-commerce platform.
Question sources
Hand-written, drawing on common ML/DS interview patterns, academic fundamentals, and industry practice. The company tags indicate where similar questions are commonly reported, not that any specific question was asked verbatim.
License
MIT
Author
Built by Lorenzo Scaturchio