Welcome to the ultimate collection of AI and ML resources! Whether you're a beginner or an experienced developer, this curated list will guide you through some of the best tools, libraries, tutorials, and communities to enhance your AI and ML skills.
- Learning AI and ML
- AI and ML Tutorials
- AI and ML Libraries
- AI and ML Frameworks
- AI and ML Tools
- AI and ML Communities
- Machine Learning Crash Course by Google: A self-study guide for aspiring machine learning practitioners.
- Introduction to Artificial Intelligence by Udacity: Learn the basics of AI with this introductory course.
- Coursera - AI For Everyone: A non-technical course that explains the basics of AI and its applications.
- Deep Learning Specialization by Coursera: A comprehensive deep learning course taught by Andrew Ng.
- fast.ai: Practical deep learning for coders with courses and tutorials.
- Kaggle Learn: Hands-on machine learning tutorials with Kaggle.
- CS229: Machine Learning by Stanford University: Advanced machine learning course by Stanford University.
- Deep Reinforcement Learning by UC Berkeley: A course on deep reinforcement learning.
- MIT OpenCourseWare - Artificial Intelligence: Advanced AI course by MIT.
- TensorFlow Tutorials: Official TensorFlow tutorials for learning machine learning and deep learning.
- PyTorch Tutorials: Official PyTorch tutorials for various machine learning tasks.
- Scikit-learn Tutorials: Official tutorials for Scikit-learn, a popular machine learning library in Python.
- TensorFlow: An end-to-end open-source platform for machine learning.
- PyTorch: An open-source machine learning library based on the Torch library.
- Scikit-learn: A Python module integrating a wide range of state-of-the-art machine learning algorithms.
- Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
- TensorFlow Extended (TFX): An end-to-end platform for deploying production machine learning pipelines.
- Apache MXNet: A scalable deep learning framework.
- ONNX (Open Neural Network Exchange): An open format built to represent machine learning models.
- Jupyter Notebook: An open-source web application for creating and sharing documents with live code.
- Google Colab: A free Jupyter notebook environment that runs in the cloud.
- Anaconda: A distribution of Python and R for scientific computing and data science.
- AI Alignment Forum: A community dedicated to AI alignment research.
- r/MachineLearning on Reddit: A subreddit for machine learning discussions and news.
- Kaggle: A platform for data science competitions and collaboration.
- AI and ML Discord Communities: A place to discuss AI and ML topics.
This resource collection is licensed under the MIT License. See the LICENSE file for details.
For any inquiries or issues, please contact drahulsingh.
Feel free to contribute to this list by opening an issue or a pull request with your suggestions. Let's make this repository a comprehensive guide to AI and ML resources for developers worldwide!
ai, machine-learning, deep-learning, data-science, tensorflow, pytorch, scikit-learn, keras, nlp, neural-networks, big-data, artificial-intelligence, data-visualization, jupyter-notebook, kaggle, reinforcement-learning, computer-vision, tensorflow-extended, onnx, mxnet