Scikit Learn
Performs wonderfully and serves as a machine learning tool in Python. We use this tool commercially with an AI library. It is just another Python library that supports both supervised and unsupervised machine learning. Most importantly, it supports regression, classification, and algorithm clustering with great model selection, dimensionality reduction, and preprocessing. It is one of the simplest and most efficient tools for data mining and data analysis. It is easily accessible and reusable in various contexts.
TensorFlow
With TensorFlow, we provide an open-source machine learning framework for everyone. It provides high-performance computation and has a Python library that invokes C++ to develop and implement dataflow graphs. It is very supportive when classification and regression algorithms need to be performed and especially for deep learning and neural networks. Backed by Google, TensorFlow can be learned and used on Colaboratory, that is Jupyter notebook environment running in the cloud and requires zero set-ups.
PyTorch
PyTorch is an open-source deep learning platform offering a seamless path from research prototyping to production deployment. Mainly developed by Facebook’s AI research team, PyTorch supports GPU and CPU computations together and provides scalable distributed training and performance optimization in production and research. The best features are GPU acceleration and deep neural networks. With these comprehensive tools, PyTorch can provide abundant resources that support development.
Keras
We prefer Keras due to its high-level neural network API that can even run on top of other popular AI/ML tools like TensorFlow or Theano. It has Python deep learning library that supports quick experimentation and claims to move from idea to the result in the least possible delay. Rather than providing a complete ML framework, Keras operates with special functionality as a user-friendly and compatible interface that takes care of modularity and total expressiveness.
