# Deep-ML

> Deep-ML is a free, open-source machine learning challenge platform where students, data scientists, and AI engineers practice ML skills through hands-on coding problems, real-dataset labs, and interactive articles.

## About

Deep-ML helps users build practical machine learning skills by solving coding challenges that cover fundamental and advanced ML topics. Unlike generic coding platforms, Deep-ML focuses specifically on machine learning, from linear algebra and calculus foundations to deep neural networks, computer vision, and natural language processing.

All problems are created by ML professionals and open-sourced at https://github.com/Open-Deep-ML/DML-OpenProblem.

## Key Features

- **Practice Problems**: 100+ ML coding challenges across beginner, intermediate, and advanced difficulty levels. Categories include linear algebra, machine learning, deep learning, NLP, and computer vision.
- **Labs**: Real-world ML projects where users implement algorithms and test them against actual datasets with evaluation metrics.
- **Articles**: Interactive educational content with math visualizations (KaTeX), code examples, and practice problems embedded inline.
- **Collections**: Curated sets of problems organized by topic (e.g., "Neural Network Basics", "Linear Algebra Essentials") with progress tracking and badges.
- **Learning Paths**: Structured courses that guide users through ML topics step by step.
- **Leaderboard**: Community rankings based on problems solved and accuracy.
- **Contests**: Timed ML coding competitions with prizes, rankings, and multiple difficulty levels.
- **Jobs Board**: Machine learning and data science job listings from companies hiring ML/AI engineers.
- **Interview Prep**: Company-specific machine learning interview preparation. Pick a company, follow a paced interview path, build the resume projects that company screens for, run timed mock interviews, and track your readiness.
- **Premium**: Exclusive collections, advanced labs, and priority features.

## Who It's For

- Students learning machine learning fundamentals
- Data scientists sharpening their implementation skills
- AI engineers preparing for ML interviews
- Anyone transitioning into machine learning careers

## How It Works

Users write Python solutions in a browser-based code editor. Solutions are evaluated against test cases using Pyodide (in-browser Python). Problems include explanations, starter code, learn sections, and hints.

## Topics Covered

- Machine Learning Fundamentals (regression, classification, clustering, dimensionality reduction)
- Deep Neural Networks (CNNs, RNNs, transformers, attention mechanisms)
- Computer Vision (image classification, object detection, segmentation)
- Natural Language Processing (tokenization, embeddings, sequence models)
- Linear Algebra (matrix operations, eigenvalues, SVD)
- Calculus for ML (derivatives, gradients, backpropagation)
- Optimization (gradient descent, Adam, learning rate scheduling)

## Interview Prep

Deep-ML's Interview Prep helps candidates land machine learning roles at specific companies. Each company track explains the interview process and rounds, recommends the resume projects that company screens for, provides timed mock interviews, and tracks readiness against a paced study plan (2 weeks to 3 months). Browse every company track at the Interview Prep hub.

## Links

- [Interview Prep](https://www.deep-ml.com/interview-prep): Company-specific ML interview preparation tracks
- [Problems](https://www.deep-ml.com/problems): Browse 100+ ML coding challenges by difficulty and category
- [Labs](https://www.deep-ml.com/labs): Real-world ML projects with actual datasets
- [Articles](https://www.deep-ml.com/articles): Interactive educational content with math visualizations
- [Collections](https://www.deep-ml.com/collections): Curated problem sets with progress tracking
- [Learn](https://www.deep-ml.com/learn): Structured learning paths for ML topics
- [Contests](https://www.deep-ml.com/contests): Timed ML coding competitions
- [Jobs](https://www.deep-ml.com/jobs): ML and data science job listings
- [Leaderboard](https://www.deep-ml.com/leaderboard): Community rankings
- [Premium](https://www.deep-ml.com/premium): Unlock exclusive features
- [FAQ](https://www.deep-ml.com/faq): Common questions answered
- [GitHub](https://github.com/Open-Deep-ML/DML-OpenProblem): Open-source problem repository
- [Twitter/X](https://x.com/real_deep_ml): Follow for updates
- [Discord](https://discord.com/invite/v9NwJjpGKK): Community server
- [LinkedIn](https://www.linkedin.com/company/deep-machine-learning/): Company page
- Email: info@deep-ml.com