Empowering developers and data scientists worldwide with production-ready AI/ML skills through hands-on, industry-focused education.
Deep Knowledge bridges the gap between theory and production, providing comprehensive learning paths that take you from fundamentals to deploying scalable AI systems in real-world environments.
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Learn skills that matter in industry. Every course focuses on building systems that work at scale, not just proof-of-concepts. |
Build real projects with actual datasets. No toy examples - work with industrial-grade problems and solutions. |
From data to deployment. Master the entire ML lifecycle including CI/CD, monitoring, and cloud infrastructure. |
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Build production-ready anomaly detection systems for computer vision applications. Master defect detection, quality control, and visual inspection using state-of-the-art deep learning architectures. 🎯 What You'll Build:
🛠️ Tech Stack: PyTorch • OpenCV • FastAPI • Docker • MLflow 📊 Level: Intermediate to Advanced |
⭐ Popular |
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Complete PyTorch mastery from fundamentals to deploying models at scale. Learn neural networks, CNNs, RNNs, Transformers, and production MLOps practices. 🎯 What You'll Master:
🛠️ Tech Stack: PyTorch • TorchScript • ONNX • TensorBoard • Ray 📊 Level: Beginner to Advanced |
🏆 Comprehensive |
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Build enterprise-grade MLOps pipelines for industrial anomaly detection. Master CI/CD, model versioning, monitoring, and deployment strategies for manufacturing environments. 🎯 What You'll Deploy:
🛠️ Tech Stack: MLflow • Kubernetes • Airflow • Prometheus • Grafana 📊 Level: Advanced |
💼 Industry Focus |
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Master computer vision fundamentals with Python. Learn image processing, feature extraction, object detection, and segmentation using OpenCV and modern deep learning frameworks. 🎯 What You'll Learn:
🛠️ Tech Stack: OpenCV • PIL • scikit-image • PyTorch • YOLO 📊 Level: Beginner to Intermediate |
🎓 Foundation |
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Complete guide to ML algorithms and techniques. Master supervised and unsupervised learning, model evaluation, feature engineering, and practical implementations. 🎯 What You'll Master:
🛠️ Tech Stack: scikit-learn • XGBoost • LightGBM • Pandas • NumPy 📊 Level: Beginner to Intermediate |
📊 Essential |
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Master Azure ML services for scalable machine learning solutions. Learn to train, deploy, and manage models on Azure cloud with enterprise best practices. 🎯 What You'll Deploy:
🛠️ Tech Stack: Azure ML • AKS • Azure Functions • Azure DevOps • Terraform 📊 Level: Intermediate to Advanced |
☁️ Cloud Native |
# Python 3.8 or higher
python --version
# Git
git --version
# Docker (optional, for containerized projects)
docker --version# 1. Clone the repository
git clone https://github.com/DeepKnowledge1/<repo_name>.git
cd <repo_name>
# 2. Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 3. Install dependencies
pip install -r requirements.txt
# 4. Verify installation
python -c "import torch; print(f'PyTorch {torch.__version__}')"📦 <repo_name>
├── 📂 src/ # Source code
│ ├── 📂 models/ # Model architectures
│ ├── 📂 data/ # Data processing
│ ├── 📂 training/ # Training scripts
│ └── 📂 inference/ # Inference pipelines
├── 📂 notebooks/ # Jupyter notebooks
├── 📂 configs/ # Configuration files
├── 📂 tests/ # Unit tests
├── 📂 docker/ # Docker configurations
├── 📂 docs/ # Documentation
├── 📜 requirements.txt # Python dependencies
├── 📜 Makefile # Common commands
└── 📜 README.md # This file
We ❤️ contributions! Here's how you can help:
- 🐛 Report Bugs - Found an issue? Open a bug report
- 💡 Suggest Features - Have an idea? Request a feature
- 📝 Improve Docs - Help us make documentation better
- 🔧 Submit PRs - Fix bugs or add features
- Fork the repository
- Create a feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
📖 Read our Contributing Guidelines for detailed information.
- 📧 Email - deepp.knowledge@gmail.com
- 🐛 GitHub Issues - Bug reports and feature requests
- 📖 Documentation - Comprehensive guides and tutorials
This project is licensed under the MIT License - see the LICENSE file for details.
MIT License - feel free to use this code for learning and commercial projects!
If you find Deep Knowledge valuable, consider supporting us:
- 🤖 Reinforcement Learning - Deep RL algorithms and applications
- 🗣️ NLP & Transformers - BERT, GPT, and modern language models
- 📱 Edge AI - Deploy models on mobile and IoT devices
- 🎮 MLOps Advanced - Advanced monitoring and automation
- 🌐 Web App Deployment - FastAPI, Streamlit, and cloud hosting
- Advanced Computer Vision (GANs, Diffusion Models)
- Time Series Forecasting
- Recommender Systems
- AutoML and Neural Architecture Search
- AI Ethics and Responsible AI
💡 Suggest a topic - Open an issue with your ideas!
Special thanks to:
- 🌟 Our Contributors - For making this project better
- 👥 Our Community - For feedback and support
- 📚 Open Source Community - For amazing tools and libraries
Start with any course above, follow along on YouTube, and join our community!
Made with ❤️ and ☕ by the Deep Knowledge Team
Transforming learners into production-ready AI engineers
