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| 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) | |