Here are the top 10 free machine learning platforms available:

1. TensorFlow: Developed by Google, TensorFlow is a widely used open-source machine learning library that provides a comprehensive ecosystem for developing and deploying ML models.

2. PyTorch: PyTorch is another popular open-source deep learning framework known for its dynamic computational graph and strong support for GPU acceleration.

3. Scikit-learn: Scikit-learn is a simple and efficient open-source library for machine learning in Python. It provides a wide range of algorithms and tools for various tasks such as classification, regression, clustering, and more.

4. Keras: Keras is a high-level neural networks API written in Python. It offers an easy-to-use interface and can be used as a front-end framework for TensorFlow, Theano, or CNTK.

5. Microsoft Azure Machine Learning: Azure Machine Learning is a cloud-based service provided by Microsoft. It offers a comprehensive platform for building, training, and deploying ML models using various tools and frameworks.

6. Google Cloud AutoML: Google Cloud AutoML allows users to build custom machine learning models without requiring extensive knowledge of ML. It offers automated model training and deployment on Google Cloud Platform.

7. IBM Watson Studio: Watson Studio is an integrated environment provided by IBM for data science and machine learning. It allows users to build and deploy ML models using popular frameworks like TensorFlow and PyTorch.

8. is an open-source machine learning platform that provides a scalable and distributed environment for building ML models. It supports various algorithms and integration with popular programming languages.

9. RapidMiner: RapidMiner is a powerful data science platform that offers a visual interface for building and deploying ML models. It provides a wide range of tools and techniques for data preprocessing, modeling, and evaluation.

10. KNIME: KNIME is an open-source platform for data analytics and machine learning. It allows users to visually assemble workflows and perform various tasks such as data preprocessing, modeling, and visualization.

These platforms offer different features and capabilities, so you can choose the one that best suits your needs and preferences. Remember to review their documentation and resources to get started with each platform effectively.