Machine Learning (ML) is a subset of Artificial Intelligence (AI) that allows systems to automatically learn from data and make predictions or take actions without being explicitly programmed. There are many different technologies and tools that can be used to build and deploy machine learning models, but some of the most popular ones include scikit-learn, TensorFlow, and PyTorch.
Scikit-learn is a popular open-source library for machine learning in Python. It is built on top of NumPy and SciPy and provides a wide range of machine learning algorithms for classification, regression, clustering, and dimensionality reduction. It is user-friendly and easy to use, making it a popular choice for beginners and practitioners alike.
TensorFlow is an open-source library for machine learning also developed by Google. It is used for a variety of tasks such as neural networks, natural language processing, image and video recognition, and more. It provides a high-level API for building and deploying machine learning models and is suitable for both research and production. TensorFlow can run on a variety of platforms, including CPUs, GPUs, and TPUs.
PyTorch is an open-source library for machine learning also developed by Facebook. It is similar to TensorFlow and provides a high-level API for building and deploying machine learning models. It is designed for research and experimentation and is popular among researchers and practitioners in the field of deep learning. Pytorch has a dynamic computational graph which makes it more intuitive to work with, especially for beginners.
Overall, scikit-learn, TensorFlow, and PyTorch are widely used libraries for building and deploying machine learning models. They provide powerful tools and libraries for data preprocessing, model building, and evaluation. They are widely used in industry and academia for a variety of tasks and are constantly being updated and improved.
