Here are some widely used frameworks and tools for machine learning and deep learning, each with its unique strengths:

1. TensorFlow: Created by Google, TensorFlow is a comprehensive and flexible open-source platform for building machine learning models. It supports deep learning and various other algorithms and offers tools for deployment across different platforms.
2. PyTorch: Known for its dynamic computation graph, PyTorch, developed by Facebook AI Research, is popular for its ease of use and flexibility. It’s especially favored in research settings and has a strong community and extensive documentation.
3. Keras: As a high-level neural networks API, Keras is user-friendly and allows for easy and fast prototyping. It integrates seamlessly with TensorFlow, making it an excellent choice for beginners and those looking to quickly build and test deep learning models.
4. scikit-learn: This Python library is ideal for classical machine learning. It provides simple and efficient tools for data analysis and modeling, built on top of NumPy, SciPy, and matplotlib. It’s perfect for tasks like classification, regression, and clustering.
5. XGBoost: This gradient boosting framework is renowned for its performance and efficiency. XGBoost is often used in machine learning competitions due to its ability to handle large datasets and improve model accuracy significantly.
6. H2O.ai: Offering scalable machine learning and deep learning capabilities, H2O.ai is notable for its AutoML feature, which automates the process of model selection and tuning, making it accessible to non-experts and speeding up the model development cycle.

These tools and frameworks each bring unique advantages to the table, catering to different needs and preferences in the machine learning and deep learning landscape.