11 Best AI Tools You Can Use

TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It provides tools and libraries for building and deploying machine learning models, including neural networks.

PyTorch: PyTorch is another open-source machine learning framework that is widely used, especially in research. Developed by Facebook’s AI Research lab (FAIR), it offers dynamic computation graphs and a more intuitive interface compared to TensorFlow.

scikit-learn: scikit-learn is a simple and efficient tool for data mining and data analysis. It provides a wide range of machine learning algorithms and is built on top of NumPy, SciPy, and matplotlib.

Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK). It is designed for fast experimentation with deep neural networks.

Jupyter Notebooks: Jupyter Notebooks are interactive computing environments that allow you to create and share documents that contain live code, equations, visualizations, and narrative text. They are widely used for data cleaning, transformation, and visualization tasks in AI projects.

IBM Watson Studio: IBM Watson Studio is a comprehensive platform for data scientists, developers, and subject matter experts to collaboratively and easily work with data and AI tools. It provides a suite of tools for data preparation, training AI models, and deploying them into production.

Microsoft Azure Machine Learning Studio: Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. It provides a range of AI services and supports various programming languages including Python and R.

Google Colab: Google Colab is a free cloud service based on Jupyter Notebooks for machine learning education and research. It offers free access to GPU and TPU for training machine learning models.

H2O.ai: H2O.ai is an open-source machine learning platform that provides a user-friendly interface for building machine learning models. It supports various machine learning algorithms and is designed for scalability and performance.

Apache Spark MLlib: Apache Spark MLlib is Apache Spark’s scalable machine learning library. It provides distributed implementations of various machine learning algorithms and tools for feature engineering, model evaluation, and data preprocessing.

OpenAI Gym: OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. It provides a collection of environments where you can train and test your reinforcement learning agents.

These tools offer a wide range of capabilities for building, training, and deploying AI models, and many of them are available for free with open-source licenses or through free tiers of cloud services.