What's the most useful tool for ML?

 There are many tools available for machine learning (ML), and the most useful tool will depend on the specific task and the individual's experience and preferences. However, some popular and widely used tools for ML include:

1. TensorFlow: TensorFlow is an open-source library for machine learning and deep learning developed by Google. It is widely used for building and deploying ML models, and has a large community of users and contributors.
2. PyTorch: PyTorch is an open-source library for machine learning and deep learning developed by Facebook. It is similar to TensorFlow and is also widely used for building and deploying ML models.
3. scikit-learn: scikit-learn is an open-source library for machine learning in Python. It is simple to use and contains a wide range of tools for tasks such as regression, classification, and clustering.
4. R: R is a programming language and environment for statistical computing and graphics. It has a wide range of packages and libraries for machine learning and data analysis, making it a popular choice among data scientists and statisticians.
5. AutoML: AutoML is a tool that automates the process of building, evaluating, and optimizing machine learning models. It is designed to make it easier for non-experts to build and deploy ML models, and it has been adopted by many organizations and companies.
6. Jupyter Notebook: Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. It is widely used for data cleaning, transformation, visualization, and machine learning.

These are just a few examples of the most popular and useful tools for ML. The best tool for a specific task will depend on the requirements, the data, and the user's experience and preferences.