Learn • Build • Research • Deploy
MLVerse-Math is a community-driven open-source ecosystem dedicated to advancing knowledge in:
- Machine Learning
- Deep Learning
- Reinforcement Learning
- Generative AI
- Large Language Models (LLMs)
- AI Agents
- MLOps
- Computer Vision
- Natural Language Processing
- Time Series Forecasting
Our mission is simple:
Build the world's most comprehensive open-source AI learning and research ecosystem.
We envision a future where every AI enthusiast, student, researcher, and engineer can learn, contribute, and innovate through a single open-source platform.
MLVerse
│
├── mlverse-machine-learning
├── mlverse-deep-learning
├── mlverse-reinforcement-learning
├── mlverse-generative-ai
├── mlverse-ai-agents
├── mlverse-llms
├── mlverse-mlops
├── mlverse-roadmaps
├── mlverse-research
├── mlverse-benchmarks
└── mlverse-docs
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- XGBoost
- LightGBM
- CatBoost
- Support Vector Machines
- Clustering Algorithms
- Dimensionality Reduction
- Artificial Neural Networks
- CNNs
- RNNs
- LSTMs
- GRUs
- Autoencoders
- Transformers
- Q-Learning
- SARSA
- DQN
- DDQN
- PPO
- A2C
- A3C
- SAC
- TD3
- Multi-Agent RL
- Large Language Models
- RAG Systems
- Fine-Tuning
- LoRA
- QLoRA
- AI Agents
- Multimodal AI
- Prompt Engineering
- Docker
- FastAPI
- MLflow
- CI/CD
- Kubernetes
- Monitoring
- Model Deployment
MLVerse provides structured learning paths for:
- AI Engineer
- Machine Learning Engineer
- Data Scientist
- Research Scientist
- Generative AI Engineer
- MLOps Engineer
- Reinforcement Learning Researcher
Our research initiatives include:
- Algorithm Implementations
- Research Paper Reproductions
- Benchmark Studies
- Optimization Techniques
- Cloud Computing & Scheduling
- Reinforcement Learning Systems
- Generative AI Research
Each algorithm implementation follows a structured format:
Algorithm/
│
├── README.md
├── Theory.md
├── Mathematics.md
├── FromScratch.ipynb
├── Framework_Implementation.ipynb
├── Visualization.ipynb
├── UseCases.md
├── InterviewQuestions.md
├── ResearchPapers.md
└── References.md
We welcome contributors from all backgrounds.
Ways to contribute:
- Add new algorithms
- Improve documentation
- Create visualizations
- Implement research papers
- Build benchmarks
- Fix bugs
- Improve tutorials
Check our contribution guidelines before getting started.
- 100 Machine Learning Algorithms
- 50 Deep Learning Models
- 25 Reinforcement Learning Algorithms
- 50 Research Paper Implementations
- AI Roadmaps
- Documentation Portal
- Benchmark Hub
- Interactive Learning Platform
- AI Research Community
- Open Source Mentorship Program
Understand the theory and mathematics behind AI.
Implement algorithms from scratch and using frameworks.
Explore state-of-the-art methods and papers.
Take models from experimentation to production.
We are building an open ecosystem where learners, researchers, and engineers collaborate to advance AI education and innovation.
Whether you are:
- A Student
- An AI Engineer
- A Researcher
- An Open Source Contributor
There is a place for you in MLVerse.
Shivam Singh
Founder of MLVerse-Math
Building the future of open-source AI education, research, and deployment.