---
title: ML Interview Prep
emoji: 🎯
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 5.9.1
python_version: "3.10"
app_file: app.py
pinned: false
license: mit
short_description: Practice ML and Data Science interview questions
---

# ML Interview Prep

An interactive tool for practicing machine learning and data science interview questions. Features 500+ curated questions across 10 categories with detailed expert answers.

## Features

### 500+ Interview Questions
Comprehensive coverage of ML/DS interview topics from top tech companies.

### 10 Categories
- Statistics & Probability
- ML Theory & Algorithms
- Deep Learning
- Natural Language Processing
- Computer Vision
- System Design
- SQL & Databases
- Python Programming
- Feature Engineering
- A/B Testing & Experimentation

### Three Difficulty Levels
- **Easy** - Fundamentals and basic concepts
- **Medium** - Applied knowledge and trade-offs
- **Hard** - Advanced topics and edge cases

### Practice Modes

**Quiz Mode**
- Random questions based on your filters
- Try to answer before revealing the solution
- Track your progress

**Flashcard Mode**
- Quick review of key concepts
- Flip cards to see answers
- Great for last-minute prep

**Browse Mode**
- Search and filter all questions
- Study specific topics in depth

### Company Tags
Questions tagged by company (Google, Meta, Amazon, etc.) so you can focus on company-specific prep.

## How to Use

1. **Select categories** you want to practice
2. **Choose difficulty** level
3. **Pick a mode** (Quiz, Flashcard, or Browse)
4. **Start practicing!**

## Question Sources

Questions are curated from:
- Real interview experiences shared online
- Common ML/DS interview patterns
- Academic fundamentals
- Industry best practices

## Example Questions

**ML Theory (Medium):**
> "Explain the bias-variance tradeoff and how it affects model selection."

**Deep Learning (Hard):**
> "How would you handle class imbalance in a neural network for fraud detection?"

**System Design (Hard):**
> "Design a real-time recommendation system for a streaming platform."

## License

MIT

## Author

Built by [Lorenzo Scaturchio](https://huggingface.co/gr8monk3ys)